{"title":"Data mining Books","description":"","products":[{"product_id":"computer-age-statistical-inference-student-edition-9781108823418","title":"Computer Age Statistical Inference Student","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. ''Data science'' and ''machine learning'' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov Chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. Each chapter ends with class-tested exercises, and the book concludes with speculation on the future direction of statistics and data science.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart I. Classic Statistical Inference: 1. Algorithms and inference; 2. Frequentist inference; 3. Bayesian inference; 4. Fisherian inference and maximum likelihood estimation; 5. Parametric models and exponential families; Part II. Early Computer-Age Methods: 6. Empirical Bayes; 7. James–Stein estimation and ridge regression; 8. Generalized linear models and regression trees; 9. Survival analysis and the EM algorithm; 10. The jackknife and the bootstrap; 11. Bootstrap confidence intervals; 12. Cross-validation and Cp estimates of prediction error; 13. Objective Bayes inference and Markov chain Monte Carlo; 14. Statistical inference and methodology in the postwar era; Part III. Twenty-First-Century Topics: 15. Large-scale hypothesis testing and false-discovery rates; 16. Sparse modeling and the lasso; 17. Random forests and boosting; 18. Neural networks and deep learning; 19. Support-vector machines and kernel methods; 20. Inference after model selection; 21. Empirical Bayes estimation strategies; Epilogue; References; Author Index; Subject Index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738337259863,"sku":"9781108823418","price":30.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781108823418.jpg?v=1723811944"},{"product_id":"machine-learning-for-time-series-forecasting-with-python-9781119682363","title":"Machine Learning for Time Series Forecasting with","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eLearn how to apply the principles of machine learning totime series modeling with thisindispensableresource Machine Learning for Time Series Forecasting with Pythonis an incisive and straightforward examination of one of the most crucial elements of decision-makingin finance,marketing,education, and healthcare:time series modeling. Despitethe centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguishedmachine learning scientistandeconomist,corrects that deficiency by providing readers withcomprehensiveand approachableexplanation andtreatment of the applicationof machine learning to time series forecasting. Written for readers who have little to no experience in time seriesforecastingor machine learning, the book comprehensively coversall the topics necessary to: Understand time series forecasting concepts, such asstationarity,horizon,trend,and seasonalityPrepare time series dataformodelingEvaluatetime series forecasting models'performance and accuracyUnderstand when to use neural networks instead of traditional time series models in time series forecasting Machine Learning for Time Series Forecasting with Pythonis fullreal-world examples, resourcesand concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts. Perfect for entry-level data scientists, business analysts,developers, and researchers, this book is an invaluable and indispensable guide to the fundamental and advanced concepts of machine learning applied to time series modeling.  \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eAcknowledgments vii\u003c\/p\u003e \u003cp\u003eIntroduction xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Overview of Time Series Forecasting 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFlavors of Machine Learning for Time Series Forecasting 3\u003c\/p\u003e \u003cp\u003eSupervised Learning for Time Series Forecasting 14\u003c\/p\u003e \u003cp\u003ePython for Time Series Forecasting 21\u003c\/p\u003e \u003cp\u003eExperimental Setup for Time Series Forecasting 24\u003c\/p\u003e \u003cp\u003eConclusion 26\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 How to Design an End-to-End Time Series Forecasting Solution on the Cloud 29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTime Series Forecasting Template 31\u003c\/p\u003e \u003cp\u003eBusiness Understanding and Performance Metrics 33\u003c\/p\u003e \u003cp\u003eData Ingestion 36\u003c\/p\u003e \u003cp\u003eData Exploration and Understanding 39\u003c\/p\u003e \u003cp\u003eData Pre-processing and Feature Engineering 40\u003c\/p\u003e \u003cp\u003eModeling Building and Selection 42\u003c\/p\u003e \u003cp\u003eAn Overview of Demand Forecasting Modeling Techniques 44\u003c\/p\u003e \u003cp\u003eModel Evaluation 46\u003c\/p\u003e \u003cp\u003eModel Deployment 48\u003c\/p\u003e \u003cp\u003eForecasting Solution Acceptance 53\u003c\/p\u003e \u003cp\u003eUse Case: Demand Forecasting 54\u003c\/p\u003e \u003cp\u003eConclusion 58\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Time Series Data Preparation 61\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePython for Time Series Data 62\u003c\/p\u003e \u003cp\u003eCommon Data Preparation Operations for Time Series 65\u003c\/p\u003e \u003cp\u003eTime stamps vs. Periods 66\u003c\/p\u003e \u003cp\u003eConverting to Timestamps 69\u003c\/p\u003e \u003cp\u003eProviding a Format Argument 70\u003c\/p\u003e \u003cp\u003eIndexing 71\u003c\/p\u003e \u003cp\u003eTime\/Date Components 76\u003c\/p\u003e \u003cp\u003eFrequency Conversion 78\u003c\/p\u003e \u003cp\u003eTime Series Exploration and Understanding 79\u003c\/p\u003e \u003cp\u003eHow to Get Started with Time Series Data Analysis 79\u003c\/p\u003e \u003cp\u003eData Cleaning of Missing Values in the Time Series 84\u003c\/p\u003e \u003cp\u003eTime Series Data Normalization and Standardization 86\u003c\/p\u003e \u003cp\u003eTime Series Feature Engineering 89\u003c\/p\u003e \u003cp\u003eDate Time Features 90\u003c\/p\u003e \u003cp\u003eLag Features and Window Features 92\u003c\/p\u003e \u003cp\u003eRolling Window Statistics 95\u003c\/p\u003e \u003cp\u003eExpanding Window Statistics 97\u003c\/p\u003e \u003cp\u003eConclusion 98\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Introduction to Autoregressive and Automated Methods for Time Series Forecasting 101\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAutoregression 102\u003c\/p\u003e \u003cp\u003eMoving Average 119\u003c\/p\u003e \u003cp\u003eAutoregressive Moving Average 120\u003c\/p\u003e \u003cp\u003eAutoregressive Integrated Moving Average 122\u003c\/p\u003e \u003cp\u003eAutomated Machine Learning 129\u003c\/p\u003e \u003cp\u003eConclusion 136\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Introduction to Neural Networks for Time Series Forecasting 137\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReasons to Add Deep Learning to Your Time Series Toolkit 138\u003c\/p\u003e \u003cp\u003eDeep Learning Neural Networks Are Capable of Automatically Learning and Extracting Features from Raw and Imperfect Data 140\u003c\/p\u003e \u003cp\u003eDeep Learning Supports Multiple Inputs and Outputs 142\u003c\/p\u003e \u003cp\u003eRecurrent Neural Networks Are Good at Extracting Patterns from Input Data 143\u003c\/p\u003e \u003cp\u003eRecurrent Neural Networks for Time Series Forecasting 144\u003c\/p\u003e \u003cp\u003eRecurrent Neural Networks 145\u003c\/p\u003e \u003cp\u003eLong Short-Term Memory 147\u003c\/p\u003e \u003cp\u003eGated Recurrent Unit 148\u003c\/p\u003e \u003cp\u003eHow to Prepare Time Series Data for LSTMs and GRUs 150\u003c\/p\u003e \u003cp\u003eHow to Develop GRUs and LSTMs for Time Series Forecasting 154\u003c\/p\u003e \u003cp\u003eKeras 155\u003c\/p\u003e \u003cp\u003eTensorFlow 156\u003c\/p\u003e \u003cp\u003eUnivariate Models 156\u003c\/p\u003e \u003cp\u003eMultivariate Models 160\u003c\/p\u003e \u003cp\u003eConclusion 164\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Model Deployment for Time Series Forecasting 167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExperimental Set Up and Introduction to Azure Machine Learning SDK for Python 168\u003c\/p\u003e \u003cp\u003eWorkspace 169\u003c\/p\u003e \u003cp\u003eExperiment 169\u003c\/p\u003e \u003cp\u003eRun 169\u003c\/p\u003e \u003cp\u003eModel 170\u003c\/p\u003e \u003cp\u003eCompute Target, RunConfiguration, and ScriptRun Config 171\u003c\/p\u003e \u003cp\u003eImage and Webservice 172\u003c\/p\u003e \u003cp\u003eMachine Learning Model Deployment 173\u003c\/p\u003e \u003cp\u003eHow to Select the Right Tools to Succeed with Model Deployment 175\u003c\/p\u003e \u003cp\u003eSolution Architecture for Time Series Forecasting with Deployment Examples 177\u003c\/p\u003e \u003cp\u003eTrain and Deploy an ARIMA Model 179\u003c\/p\u003e \u003cp\u003eConfigure the Workspace 182\u003c\/p\u003e \u003cp\u003eCreate an Experiment 183\u003c\/p\u003e \u003cp\u003eCreate or Attach a Compute Cluster 184\u003c\/p\u003e \u003cp\u003eUpload the Data to Azure 184\u003c\/p\u003e \u003cp\u003eCreate an Estimator 188\u003c\/p\u003e \u003cp\u003eSubmit the Job to the Remote Cluster 188\u003c\/p\u003e \u003cp\u003eRegister the Model 189\u003c\/p\u003e \u003cp\u003eDeployment 189\u003c\/p\u003e \u003cp\u003eDefine Your Entry Script and Dependencies 190\u003c\/p\u003e \u003cp\u003eAutomatic Schema Generation 191\u003c\/p\u003e \u003cp\u003eConclusion 196\u003c\/p\u003e \u003cp\u003eReferences 197\u003c\/p\u003e \u003cp\u003eIndex 199\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48738363801943,"sku":"9781119682363","price":35.62,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119682363.jpg?v=1723811979"},{"product_id":"teach-yourself-visually-power-bi-9781119903772","title":"Teach Yourself Visually Power Bi","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eChapter 1 Getting Started with Power BI\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Is Power BI? 4\u003c\/p\u003e \u003cp\u003eUnderstanding the Different Components of Power BI 6\u003c\/p\u003e \u003cp\u003eUnderstanding Power BI as Part of the Power Platform 7\u003c\/p\u003e \u003cp\u003eInstall Power BI Desktop 8\u003c\/p\u003e \u003cp\u003eStart and Pin Power BI Desktop 10\u003c\/p\u003e \u003cp\u003eExplore the Power BI Workspace 12\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Connecting Power BI to Your Data\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGrasp How Power Query Editor Works with Power BI Desktop 18\u003c\/p\u003e \u003cp\u003eConnect Power BI Desktop to a Local File 20\u003c\/p\u003e \u003cp\u003eSave, Close, and Open Power BI Reports 22\u003c\/p\u003e \u003cp\u003eStart Working with the Sample Dataset 24\u003c\/p\u003e \u003cp\u003eConnect to a Power BI Dataset 28\u003c\/p\u003e \u003cp\u003eConnect to a SharePoint List 30\u003c\/p\u003e \u003cp\u003eConnect to a SQL Server Database 34\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Cleaning and Shaping Data\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRemove Duplicate Values 40\u003c\/p\u003e \u003cp\u003eReplace Values in a Column 42\u003c\/p\u003e \u003cp\u003eSplit a Column Using a Delimiter 44\u003c\/p\u003e \u003cp\u003eGroup Data 46\u003c\/p\u003e \u003cp\u003eAdd a Calculated Column 48\u003c\/p\u003e \u003cp\u003eAdd an Index Column 50\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Modeling Data in Model View\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCreate Dimension Tables 54\u003c\/p\u003e \u003cp\u003eCreate Relationships Between Tables 58\u003c\/p\u003e \u003cp\u003eCreate a Star Schema 62\u003c\/p\u003e \u003cp\u003eCreate a Hierarchical Schema 64\u003c\/p\u003e \u003cp\u003eUsing the Properties Pane 70\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Creating Basic Visualizations\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCreate a Bar Chart 74\u003c\/p\u003e \u003cp\u003eApply Filters to Visuals 76\u003c\/p\u003e \u003cp\u003eFormat the Y-Axis of a Bar Chart 78\u003c\/p\u003e \u003cp\u003eFormat the X-Axis of a Bar Chart 80\u003c\/p\u003e \u003cp\u003eAdd and Format the Data Category of a Bar Chart 82\u003c\/p\u003e \u003cp\u003eMove a Bar Chart’s Legend and Add Gridlines 84\u003c\/p\u003e \u003cp\u003eAdd a Zoom Slider and Update Bar Colors 86\u003c\/p\u003e \u003cp\u003eAdd Data Labels to a Bar Chart 88\u003c\/p\u003e \u003cp\u003eAdd an Image to the Plot Area Background 90\u003c\/p\u003e \u003cp\u003eCreate a Line Chart or Area Chart 92\u003c\/p\u003e \u003cp\u003eFormat the Axes of a Line or Area Chart 94\u003c\/p\u003e \u003cp\u003eAdd a Legend to a Line or Area Chart 96\u003c\/p\u003e \u003cp\u003eMove the Legend and Add Gridlines to a Line or Area Chart 98\u003c\/p\u003e \u003cp\u003eAdd a Zoom Slider and Steps to a Line or Area Chart 100\u003c\/p\u003e \u003cp\u003eAdd Data Markers and Labels to a Line or Area Chart 102\u003c\/p\u003e \u003cp\u003eFormat the Data Labels of a Line or Area Chart 104\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Creating Advanced Data Visualizations\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCreate and Format a Gauge Chart 108\u003c\/p\u003e \u003cp\u003eCreate and Format a KPI Visual 112\u003c\/p\u003e \u003cp\u003eCreate a Matrix Visual 116\u003c\/p\u003e \u003cp\u003eFormat a Matrix Visual 118\u003c\/p\u003e \u003cp\u003eFormat the Values and Column Headers of a Matrix Visual 120\u003c\/p\u003e \u003cp\u003eFormat the Row Headers of a Matrix Visual 122\u003c\/p\u003e \u003cp\u003eFormat the Row Subtotals and Grand Totals of a Matrix Visual 124\u003c\/p\u003e \u003cp\u003eFormat the Specific Column and Cell Elements of a Matrix Visual 126\u003c\/p\u003e \u003cp\u003eCreate a Waterfall Chart 128\u003c\/p\u003e \u003cp\u003eFormat a Waterfall Chart 130\u003c\/p\u003e \u003cp\u003eFormat the X-Axis and Legend of a Waterfall Chart 132\u003c\/p\u003e \u003cp\u003eAdd and Format Breakdowns in a Waterfall Chart 134\u003c\/p\u003e \u003cp\u003eCreate, Format, and Label a Funnel Chart 136\u003c\/p\u003e \u003cp\u003eCreate a Pie Chart or Donut Chart 140\u003c\/p\u003e \u003cp\u003eFormat a Pie Chart or Donut Chart 142\u003c\/p\u003e \u003cp\u003eCreate a Treemap Chart 144\u003c\/p\u003e \u003cp\u003eFormat a Treemap Chart 146\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Showing Geographic Data on Maps\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCreate a Proportional Symbol Map 150\u003c\/p\u003e \u003cp\u003eCreate a Choropleth Map 152\u003c\/p\u003e \u003cp\u003eAdd Conditional Formatting to a Choropleth Map 154\u003c\/p\u003e \u003cp\u003eEnable Power BI’s Preview Features 156\u003c\/p\u003e \u003cp\u003eCreate an Isarithmic Map 158\u003c\/p\u003e \u003cp\u003eCreate a Skyscraper Map 160\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Using Calculated Columns and DAX\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding DAX and Why You Should Use It 164\u003c\/p\u003e \u003cp\u003eAdd All Numbers in a Column 166\u003c\/p\u003e \u003cp\u003ePerform Division 168\u003c\/p\u003e \u003cp\u003eCheck a Condition 170\u003c\/p\u003e \u003cp\u003eCount the Number of Cells in a Column 172\u003c\/p\u003e \u003cp\u003eReturn the Average of All Numbers in a Column 174\u003c\/p\u003e \u003cp\u003eJoin Two Text Strings into One Text String 176\u003c\/p\u003e \u003cp\u003eApply Conditional Formatting in Tables 178\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Using Analytics and Machine Learning\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIdentify Outliers 184\u003c\/p\u003e \u003cp\u003eFind Groups of Similar Data by Clustering 186\u003c\/p\u003e \u003cp\u003eCreate a Dataflow 188\u003c\/p\u003e \u003cp\u003eApply Binary Prediction with AutoML 192\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Creating Interactive Reports\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePlanning to Create a Report 198\u003c\/p\u003e \u003cp\u003eStart a Report and Add a Title 200\u003c\/p\u003e \u003cp\u003eAdd Visuals to a Report 202\u003c\/p\u003e \u003cp\u003eAdd Slicers to a Report 206\u003c\/p\u003e \u003cp\u003eControl Which Visuals and Slicers Interact 208\u003c\/p\u003e \u003cp\u003eEnable and Control Drill-Through Actions 210\u003c\/p\u003e \u003cp\u003eSplit a Page into Sections 214\u003c\/p\u003e \u003cp\u003eAdd Bookmarks and Navigation to a Report 218\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Publishing Reports and Dashboards\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSet Up a Workspace 224\u003c\/p\u003e \u003cp\u003eAsk Questions About the Data 226\u003c\/p\u003e \u003cp\u003ePublish a Report to the Power BI Service 228\u003c\/p\u003e \u003cp\u003eSet Up Row-Level Security 230\u003c\/p\u003e \u003cp\u003eAdd Tiles to a Dashboard 232\u003c\/p\u003e \u003cp\u003eShare a Dashboard 234\u003c\/p\u003e \u003cp\u003eSchedule Data Refreshes 236\u003c\/p\u003e \u003cp\u003ePublish a Report to the Web 238\u003c\/p\u003e \u003cp\u003eIndex 240\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48738375106903,"sku":"9781119903772","price":19.54,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119903772.jpg?v=1723811991"},{"product_id":"data-quality-9781394165230","title":"Data Quality","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eDiscover how to achieve business goals by relying on high-quality, robust data\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eIn \u003ci\u003eData Quality: Empowering Businesses with Analytics and AI\u003c\/i\u003e, veteran data and analytics professional delivers a practical and hands-on discussion on how to accelerate business results using high-quality data. In the book, you'll learn techniques to define and assess data quality, discover how to ensure that your firm's data collection practices avoid common pitfalls and deficiencies, improve the level of data quality in the business, and guarantee that the resulting data is useful for powering high-level analytics and AI applications. \u003c\/p\u003e\u003cp\u003eThe author shows you how to: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eProfile for data quality, including the appropriate techniques, criteria, and KPIs \u003c\/li\u003e \u003cli\u003eIdentify the root causes of data quality issues in the business apart from discussing the 16 common root causes that degrade data quality in the organization.\u003c\/li\u003e \u003cli\u003eFormulate the reference architecture for data quality, in\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eForeword\u003c\/p\u003e \u003cp\u003eby Bill Inmon\u003c\/p\u003e \u003cp\u003ePreface\u003c\/p\u003e \u003cp\u003eAbout the Book\u003c\/p\u003e \u003cp\u003eQuality Principles Applied in This Book\u003c\/p\u003e \u003cp\u003eOrganization of the Book\u003c\/p\u003e \u003cp\u003eWho Should Read This Book?\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003eAcknowledgments\u003c\/p\u003e \u003cp\u003eDefine Phase\u003c\/p\u003e \u003cp\u003eChapter 1: Introduction\u003c\/p\u003e \u003cp\u003eIntroduction\u003c\/p\u003e \u003cp\u003eData, Analytics, AI, and Business Performance\u003c\/p\u003e \u003cp\u003eData as a Business Asset or Liability\u003c\/p\u003e \u003cp\u003eData Governance, Data Management, and Data Quality\u003c\/p\u003e \u003cp\u003eLeadership Commitment to Data Quality\u003c\/p\u003e \u003cp\u003eKey Takeaways\u003c\/p\u003e \u003cp\u003eConclusion\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003eChapter 2: Business Data\u003c\/p\u003e \u003cp\u003eIntroduction\u003c\/p\u003e \u003cp\u003eData in Business\u003c\/p\u003e \u003cp\u003eTelemetry Data\u003c\/p\u003e \u003cp\u003ePurpose of Data in Business\u003c\/p\u003e \u003cp\u003eBusiness Data Views\u003c\/p\u003e \u003cp\u003eKey Characteristics of Business Data\u003c\/p\u003e \u003cp\u003eCritical Data Elements (CDE)\u003c\/p\u003e \u003cp\u003eKey Takeaways\u003c\/p\u003e \u003cp\u003eConclusion\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003eChapter 3: Data Quality in Business\u003c\/p\u003e \u003cp\u003eIntroduction\u003c\/p\u003e \u003cp\u003eData Quality Dimensions\u003c\/p\u003e \u003cp\u003eContext in Data Quality\u003c\/p\u003e \u003cp\u003eConsequences and Costs of Poor Data Quality\u003c\/p\u003e \u003cp\u003eData Depreciation and Its Factors\u003c\/p\u003e \u003cp\u003eData in IT Systems\u003c\/p\u003e \u003cp\u003eData Quality and Trusted Information\u003c\/p\u003e \u003cp\u003eKey Takeaways\u003c\/p\u003e \u003cp\u003eConclusion\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003eAnalyze Phase\u003c\/p\u003e \u003cp\u003eChapter 4: Causes for Poor Data Quality\u003c\/p\u003e \u003cp\u003eIntroduction\u003c\/p\u003e \u003cp\u003eData Quality RCA Techniques\u003c\/p\u003e \u003cp\u003eTypical Causes of Poor Data Quality\u003c\/p\u003e \u003cp\u003eKey Takeaways\u003c\/p\u003e \u003cp\u003eConclusion\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003eChapter 5: Data Lifecycle and Lineage\u003c\/p\u003e \u003cp\u003eIntroduction\u003c\/p\u003e \u003cp\u003eBusiness-Enabled DLC Stages\u003c\/p\u003e \u003cp\u003eIT Business-Enabled DLC Stages\u003c\/p\u003e \u003cp\u003eData Lineage\u003c\/p\u003e \u003cp\u003eKey Takeaways\u003c\/p\u003e \u003cp\u003eConclusion\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003eChapter 6: Profiling for Data Quality\u003c\/p\u003e \u003cp\u003eIntroduction\u003c\/p\u003e \u003cp\u003eCriteria for Data Profiling\u003c\/p\u003e \u003cp\u003eData Profiling Techniques for Measures of Centrality\u003c\/p\u003e \u003cp\u003eData Profiling Techniques for Measures of Variation\u003c\/p\u003e \u003cp\u003eIntegrating Centrality and Variation KPIs\u003c\/p\u003e \u003cp\u003eKey Takeaways\u003c\/p\u003e \u003cp\u003eConclusion\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003eRealize Phase\u003c\/p\u003e \u003cp\u003eChapter 7: Reference Architecture for Data Quality\u003c\/p\u003e \u003cp\u003eIntroduction\u003c\/p\u003e \u003cp\u003eOptions to Remediate Data Quality\u003c\/p\u003e \u003cp\u003eDataOps\u003c\/p\u003e \u003cp\u003eData Product\u003c\/p\u003e \u003cp\u003eData Fabric and Data Mesh\u003c\/p\u003e \u003cp\u003eData Enrichment\u003c\/p\u003e \u003cp\u003eKey Takeaways\u003c\/p\u003e \u003cp\u003eConclusion\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003eChapter 8: Best Practices to Realize Data Quality\u003c\/p\u003e \u003cp\u003eIntroduction\u003c\/p\u003e \u003cp\u003eOverview of Best Practices\u003c\/p\u003e \u003cp\u003eBP 1: Identify the Business KPIs and the Ownership of These KPIs and the Pertinent Data\u003c\/p\u003e \u003cp\u003eBP 2: Build and Improve the Data Culture and Literacy in the Organization\u003c\/p\u003e \u003cp\u003eBP 3: Define the Current and Desired state of Data Quality\u003c\/p\u003e \u003cp\u003eBP 4: Follow the Minimalistic Approach to Data Capture\u003c\/p\u003e \u003cp\u003eBP 5: Select and Define the Data Attributes for Data Quality\u003c\/p\u003e \u003cp\u003eBP 6: Capture and Manage Critical Data with Data Standards in MDM Systems\u003c\/p\u003e \u003cp\u003eKey Takeaways\u003c\/p\u003e \u003cp\u003eConclusion\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003eChapter 9: Best Practices to Realize Data Quality\u003c\/p\u003e \u003cp\u003eIntroduction\u003c\/p\u003e \u003cp\u003eBP 7: Automate the Integration of Critical Data Elements\u003c\/p\u003e \u003cp\u003eBP 8: Define the SoR and Securely Capture Transactional Data in the SoR\/OLTP System\u003c\/p\u003e \u003cp\u003eBP 9: Build and Manage Robust Data Integration Capabilities\u003c\/p\u003e \u003cp\u003eBP 10: Distribute Data Sourcing and Insight Consumption\u003c\/p\u003e \u003cp\u003eKey Takeaways\u003c\/p\u003e \u003cp\u003eConclusion\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003eSustain Phase\u003c\/p\u003e \u003cp\u003eChapter 10: Data Governance\u003c\/p\u003e \u003cp\u003eIntroduction\u003c\/p\u003e \u003cp\u003eData Governance Principles\u003c\/p\u003e \u003cp\u003eData Governance Design Components\u003c\/p\u003e \u003cp\u003eImplementing the Data Governance Program\u003c\/p\u003e \u003cp\u003eData Observability\u003c\/p\u003e \u003cp\u003eData Compliance – ISO 27001 and SOC2\u003c\/p\u003e \u003cp\u003eKey Takeaways\u003c\/p\u003e \u003cp\u003eConclusion\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003eChapter 11: Protecting Data\u003c\/p\u003e \u003cp\u003eIntroduction\u003c\/p\u003e \u003cp\u003eData Classification\u003c\/p\u003e \u003cp\u003eData Safety\u003c\/p\u003e \u003cp\u003eData Security\u003c\/p\u003e \u003cp\u003eKey Takeaways\u003c\/p\u003e \u003cp\u003eConclusion\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003eChapter 12: Data Ethics\u003c\/p\u003e \u003cp\u003eIntroduction\u003c\/p\u003e \u003cp\u003eData Ethics\u003c\/p\u003e \u003cp\u003eImportance of Data Ethics\u003c\/p\u003e \u003cp\u003ePrinciples of Data Ethics\u003c\/p\u003e \u003cp\u003eModel Drift in Data Ethics\u003c\/p\u003e \u003cp\u003eData Privacy\u003c\/p\u003e \u003cp\u003eManaging Data Ethically\u003c\/p\u003e \u003cp\u003eKey Takeaways\u003c\/p\u003e \u003cp\u003eConclusion\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003eAppendix 1: Abbreviations and Acronyms\u003c\/p\u003e \u003cp\u003eAppendix 2: Glossary\u003c\/p\u003e \u003cp\u003eAppendix 3: Data Literacy Competencies\u003c\/p\u003e \u003cp\u003eAbout the Author\u003c\/p\u003e \u003cp\u003eIndex\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48738658812247,"sku":"9781394165230","price":24.79,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781394165230.jpg?v=1720049803"},{"product_id":"data-science-and-analytics-for-smes-9781484286692","title":"Data Science and Analytics for SMEs","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eMaster the tricks and techniques of business analytics consulting, specifically applicable to small-to-medium businesses (SMEs). Written to help you hone your business analytics skills, this book applies data science techniques to help solve problems and improve upon many aspects of a business'' operations. \u003cdiv\u003e\u003cbr\u003e\u003c\/div\u003e\u003cdiv\u003eSMEs are looking for ways to use data science and analytics, and this need is becoming increasingly pressing with the ongoing digital revolution. The topics covered in the books will help to provide the knowledge leverage needed for implementing data science in small business. The demand of small business for data analytics are in conjunction with the growing number of freelance data science consulting opportunities; hence this book will provide insight on how to navigate this new terrain.\u003cdiv\u003e\u003cbr\u003e\u003c\/div\u003e\n\u003cdiv\u003eThis book uses a do-it-yourself approach to analytics and introduces tools that are easily available online and are non-programming based. Data science \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“By reading the book and working out the use case, subject matter experts will be able to get a coherent roadmap to the main techniques available for both descriptive and predictive data analytics, as well as be able to provide simple services related to their company data and future prospects.” (Rosario Uceda-Sosa, Computing Reviews, October 2, 2023)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e​ INTRODUCTION\u003cbr\u003eWe introduce data science generally and narrow it down to data science for business which is also referred to as business analytics. We then give a detailed explanation of the process involved in business analytics in form of the business analytics journey. In this journey, we explain what it takes from start to finish to carry out an analytics project in the business world, focusing on small business consulting, even though the process is generic to all types of business, small or large. We also give a description of what small business refers to in this book and the peculiarities of navigating an analytics project in such a terrain.  To conclude the chapter, we talk about the types of analytics problems that is common to small business and the tools available to solve these problems given the budget situation of small businesses when it comes to analytics project.\u003cbr\u003e·         DATA SCIENCE\u003cbr\u003e·         DATA SCIENCE FOR BUSINESS\u003cbr\u003e·         BUSINESS ANALYTICS JOURNEY\u003cbr\u003e·         SMALL AND MEDIUM BUSINESS (SME)\u003cbr\u003e·         BUSINESS ANALYTICS IN SMALL BUSINESS\u003cbr\u003e·         TYPES OF ANALYTICS PROBLEMS IN SME\u003cbr\u003e·         ANALYTICS TOOLS FOR SMES\u003cbr\u003e·         ROAD MAPS TO THIS BOOK\u003cbr\u003e·         PROBLEMS\u003cbr\u003e·         REFERENCES\u003cbr\u003e \u003cbr\u003eCHAPTER 1: DATA FOR ANALYSIS IN SMALL BUSINESS\u003cbr\u003eIn this chapter, we would look at the various sources of data generally and in small business. This chapter is important because the major challenge of consulting for small business is the lack of data or quality data for analysis. This chapter will therefore detail the sources of data for analysis explaining first the type or form that data exists and some general ideas of how to collect such data. It gives an overview on data quality and integrity issues and touches on data literacy. The chapter also includes the typical data preparation procedures for the common types of techniques used in small business analytics and by extension used in this book. To conclude the chapter, we look at data visualization, particularly towards preparing data for various analytics task as explained in section 1.3.\u003cbr\u003e·         SOURCE OF DATA\u003cbr\u003e·         DATA QUALITY \u0026amp; INTEGRITY\u003cbr\u003e·         DATA GOVERNANCE\u003cbr\u003e·         DATA PREPARATION\u003cbr\u003e·         DATA VISUALIZATION\u003cbr\u003e·         PROBLEMS\u003cbr\u003e·         REFERENCES\u003cbr\u003eCHAPTER 2: BUSINESS ANALYTICS CONSULTING\u003cbr\u003eIn this chapter, we will look at business analytics consulting, particularly what the concept implies and how to build such a career path. We will explain the types of business analytics consulting that exist and then narrow it down to how to navigate the world of business analytics consulting for small business. In this chapter, we will look at how to manage a typical analytics project and measure the success of analytics projects. In conclusion, we will discuss issues revolving around how to bill analytics project particularly as a consultant.\u003cbr\u003e·         BUSINESS ANALYTICS CONSULTING\u003cbr\u003e·         MANAGING ANALYTICS PROJECT\u003cbr\u003e·         SUCCESS METRICS IN ANALYTICS PROJECT\u003cbr\u003e·         BILLING ANALYTICS PROJECT\u003cbr\u003e·         PROBLEMS\u003cbr\u003e·         REFERENCES\u003cbr\u003eCHAPTER 3: BUSINESS ANALYTICS CONSULTING PHASES\u003cbr\u003eIn this chapter we will look at the stages involved business analytics consulting, particularly when the analytics service is offered as a product from either within or outside the business. We will look at the proposal and initial analysis stage which gives direction to the analytics project. Then we look at the details involved in the pre-engagement, engagement and post engagement phase. It is important to know that the stages are presented in a typical or generic way but when implemented, there might be reason to modify or customize them for the application scenario.\u003cbr\u003e·         PROPOSAL \u0026amp; INITIAL ANALYSIS\u003cbr\u003e·         PRE- ENGAGEMENT PHASE\u003cbr\u003e·         ENGAGEMENT PHASE\u003cbr\u003e·         POST ENGAGEMENT PHASE\u003cbr\u003e·         PROBLEMS\u003cbr\u003e·         REFERENCES\u003cbr\u003e \u003cbr\u003eCHAPTER 4: DESCRIPTIVE ANALYTICS TOOLS\u003cbr\u003eThis chapter is focused on the mostly common descriptive analytics tools used in business generally and specifically in small businesses. The chapter will help  to use descriptive analytics tools to understand your business and make recommendations that can improve your business profits. For small business, descriptive analytics helps SMEs to make sense of available data in order to monitor business indicators at a glance, helps SME owners to observe sales trends and patterns on an overall basis, as well as deep-dive into product categories and customer groups.  It also helps SME’s to plan product strategy, pricing policies that will maximize their projected revenues and derive a lot of valuable insights for getting more customers.\u003cbr\u003e \u003cbr\u003e·         INTRODUCTION\u003cbr\u003e·         BAR CHART\u003cbr\u003e·         HISTOGRAM\u003cbr\u003e·         LINE GRAPHS\u003cbr\u003e·         SCATTER PLOTS\u003cbr\u003e·         PACKED BUBBLES CHARTS\u003cbr\u003e·         HEAT MAPS\u003cbr\u003e·         GEOGRAPHICAL MAPS\u003cbr\u003e·         A PRACTICAL BUSINESS PROBLEM I\u003cbr\u003e·         PROBLEMS\u003cbr\u003e·         REFERENCES\u003cbr\u003e \u003cbr\u003eCHAPTER 5: PREDICTION TECHNIQUES\u003cbr\u003eIn this chapter, we will explore the popular techniques used for prediction, particularly in retails business. The approach used in explaining these techniques us to use them in solving a business problem. The second business problem to be addressed is the sales prediction problem which is common in retail business. The chapter first explain the fundamental concept of prediction techniques, next we look at how such techniques are evaluated. After this, we describe the business problem we intend solving. We then pick each of the selected techniques one by one and explain the algorithms involved and how they can be used to solve the problem described. The prediction techniques used and compared are the Multiple linear regression, the Regression Trees and the Neural Network. To conclude the chapter, we compare the results of the three algorithms and conclude on the problem in question.  In this chapter therefore, the analytics products being offered is to solve sales prediction problem for small retail business.\u003cbr\u003e·         INTRODUCTION\u003cbr\u003e·         PRACTICAL BUSINESS PROBLEM II (SALES PREDICTION)\u003cbr\u003e·         MULTIPLE LINEAR REGRESSION\u003cbr\u003e·         REGRESSIN TREES\u003cbr\u003e·         NEURAL NETWORK (PREDICTION)\u003cbr\u003e·         CONCLUSION ON SALES PREDICTION\u003cbr\u003e·         PROBLEMS\u003cbr\u003e·         REFERENCES\u003cbr\u003e \u003cbr\u003eCHAPTER 6: CLASSIFICATION TECHNIQUES\u003cbr\u003eIn this chapter, even though there are several classification techniques, we will explore the popular ones used for classification in the business domain.  In doing this, we will use the third business problem centered on customer loyalty comparing neural network, classification tree and random forest algorithms. In solving this problem, we are particular about how to get and retain more customers for our small business.  We will also introduce some other classification based techniques such as K-nearest neighbour logistic regression and persuasion modelling.  We will use persuasion modelling for the fourth practical business problem. In using these techniques to solve the problem we explain the fundamental concepts in the chosen algorithms and use them to demonstrate how this problems solving process can be adopted in real business scenarios.\u003cbr\u003e·         CLASSIFICATION MODELS \u0026amp; EVALUATION\u003cbr\u003e·         PRACTICAL BUSINESS PROBLEM III (CUSTOMER LOYALTY)\u003cbr\u003e·         NEURAL NETWORK\u003cbr\u003e·         CLASSIFICATION TREE\u003cbr\u003e·         RANDOM FOREST \u0026amp; BOOSTED TREES\u003cbr\u003e·         K NEAREST NEIGHBOUR\u003cbr\u003e·         LOGISTIC REGRESSION\u003cbr\u003e·         PROBLEMS\u003cbr\u003e·         REFERENCES\u003cbr\u003e \u003cbr\u003eCHAPTER 7: ADVANCED DESCRIPTIVE ANALYTICS\u003cbr\u003eThis chapter is focused mainly on advanced descriptive analytics techniques. In this chapter, we will first explain the concept of clustering which is a type of unsupervised learning approach. We will then pick one clustering technique which is the K means clustering. Using the fourth practical business problem, we will explain how we can use the K means clustering technique to solve a real business problem. Next will explain the association rule example and finally Network analysis. We conclude with the fifth business problem which is focused on using network analytics for employee efficiency.\u003cbr\u003e·         CLUSTERING\u003cbr\u003e·         K MEANS\u003cbr\u003e·         PRACTICAL BUSINESS PROBLEM IV (Customer Segmentation)\u003cbr\u003e·         ASSOCIATION ANALYSIS\u003cbr\u003e·         NETWORK ANALYSIS\u003cbr\u003e·         PRACTICAL BUSINESS PROBLEM V (Staff Efficiency)\u003cbr\u003e·         PROBLEMS\u003cbr\u003e·         REFERENCES\u003cbr\u003e \u003cbr\u003eCHAPTER 8: CASE STUDY PART I\u003cbr\u003eThis chapter is the beginning part of major consulting case study for this book. We will explain what transpired during a typical business analytics consulting and help to create a road map or an example of how to navigate a business analytics consulting project. We start with a description of the SME Ecommerce environment generally, since this is the business environment of our selected case study, we then talk about the sources of data for analytics peculiar this environment. Next we describe the business to be used as case study briefly, followed by the analytics road map peculiar to consulting for this business. This chapter ends with the results of the initial analysis and pre engagement phase which forms the bases for the detailed analytics and implementation phase in chapter 10.\u003cbr\u003e·         SME ECORMERCE\u003cbr\u003e·         INTRODUCTION TO SME CASE STUDY\u003cbr\u003e·         INITIAL ANALYSIS\u003cbr\u003e·         ANALYTICS APPROACH           \u003cbr\u003e·         PRE –ENGAGEMENT\u003cbr\u003e·         PROBLEMS\u003cbr\u003e·         REFERENCES\u003cbr\u003e \u003cbr\u003eCHAPTER 9: CASE STUDY PART II\u003cbr\u003eIn this chapter, we will conclude the case study used for illustration of a typical business analytics consulting for an SME by presenting the details of the engagement phase for the case study in question. The post engagement phase is left  out as the implementation of the recommendations is determined by the systems and procedures of the business. It is important to note that the consulting steps can be customized for any small business based on the intended problem. The whole steps described in chapter 9 and 10 have been made simple for understanding, though in real life business application there might be need to iterate the process until satisfactory results have been gotten. This is because you constantly need to incorporate feedback from the stakeholders and domain experts.\u003cbr\u003e·         GOAL 1:        INCREASE WEBSITE TRAFFIC\u003cbr\u003e·         GOAL 2:       INCREASE WEBSITE SALES REVENUE\u003cbr\u003e·         PROBLEMS\u003cbr\u003e·         REFERENCES\u003c\/div\u003e\n\u003c\/div\u003e","brand":"APress","offers":[{"title":"Default Title","offer_id":48739668197719,"sku":"9781484286692","price":31.34,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781484286692.jpg?v=1720052860"},{"product_id":"google-cloud-platform-for-data-science-9781484296875","title":"Google Cloud Platform for Data Science","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book is your practical and comprehensive guide to learning Google Cloud Platform (GCP) for data science, using only the free tier services offered by the platform.   Data science and machine learning are increasingly becoming critical to businesses of all sizes, and the cloud provides a powerful platform for these applications. GCP offers a range of data science services that can be used to store, process, and analyze large datasets, and train and deploy machine learning models.   The book is organized into seven chapters covering various topics such as GCP account setup, Google Colaboratory, Big Data and Machine Learning, Data Visualization and Business Intelligence, Data Processing and Transformation, Data Analytics and Storage, and Advanced Topics. Each chapter provides step-by-step instructions and examples illustrating how to use GCP services for data science and big data projects.   Readers will learn how to set up a Google Colaboratory account and run Jupyternotebooks, access GCP services and data from Colaboratory, use BigQuery for data analytics, and deploy machine learning models using Vertex AI. The book also covers how to visualize data using Looker Data Studio, run data processing pipelines using Google Cloud Dataflow and Dataprep, and store data using Google Cloud Storage and SQL.   What You Will LearnSet up a GCP account and projectExplore BigQuery and its use cases, including machine learningUnderstand Google Cloud AI Platform and its capabilities Use Vertex AI for training and deploying machine learning modelsExplore Google Cloud Dataproc and its use cases for big data processingCreate and share data visualizations and reports with Looker Data StudioExplore Google Cloud Dataflow and its use cases for batch and stream data processing Run data processing pipelines on Cloud DataflowExplore Google Cloud Storageand its use cases for data storage Get an introduction to Google Cloud SQL and its use cases for relational databases Get an introduction to Google Cloud Pub\/Sub and its use cases for real-time data streamingWho This Book Is ForData scientists, machine learning engineers, and analysts who want to learn how to use Google Cloud Platform (GCP) for their data science and big data projects\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eChapter 1: Introduction to GCP.- Chapter 2: Google Colaboratory.- Chapter 3: Big Data and Machine Learning.- Chapter 4: Data Visualization and Business Intelligence.- Chapter 5: Data Processing and Transformation.- Chapter 6: Data Analytics and Storage.- Chapter 7: Advanced Topics.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e","brand":"APress","offers":[{"title":"Default Title","offer_id":48739670163799,"sku":"9781484296875","price":38.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781484296875.jpg?v=1720052865"},{"product_id":"if-then-how-one-data-company-invented-the-future-9781529386158","title":"If Then: How One Data Company Invented the Future","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eRadio 4's Book of the Week\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eA Financial Times Book of the Year\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eShortlisted for the 2020 Financial Times \/ McKinsey Business Book of the Year\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eLonglisted for the National Book Award \u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003e'The story of the original data science hucksters of the 1960s is hilarious, scathing and sobering - what you might get if you crossed Mad Men with Theranos' David Runciman\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eThe Simulmatics Corporation, founded in 1959, mined data, targeted voters, accelerated news, manipulated consumers, destabilized politics, and disordered knowledge--decades before Facebook, Google, Amazon, and Cambridge Analytica.  Silicon Valley likes to imagine it has no past but the scientists of Simulmatics are the long-dead grandfathers of Mark Zuckerberg and Elon Musk. \u003cbr\u003e\u003cbr\u003eBorrowing from psychological warfare, they used computers to predict and direct human behavior, deploying their \"People Machine\" from New York, Cambridge, and Saigon for clients that included John Kennedy's presidential campaign, the \u003ci\u003eNew York Times\u003c\/i\u003e, Young \u0026amp; Rubicam, and, during the Vietnam War, the Department of Defence. \u003cbr\u003e\u003cbr\u003eIn \u003ci\u003eIf Then\u003c\/i\u003e, distinguished Harvard historian and \u003ci\u003eNew Yorker \u003c\/i\u003estaff writer, Jill Lepore, unearths from the archives the almost unbelievable story of this long-vanished corporation, and of the women hidden behind it. In the 1950s and 1960s, Lepore argues, Simulmatics invented the future by building the machine in which the world now finds itself trapped and tormented, algorithm by algorithm.\u003cbr\u003e\u003cbr\u003e\u003cb\u003e'A person can't help but feel inspired by the riveting intelligence and joyful curiosity of Jill Lepore. Knowing that there is a mind like hers in the world is a hope-inducing thing' George Saunders, Man Booker Prize-winning author of \u003ci\u003eLincoln in the Bardo\u003c\/i\u003e\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003e'An authoritative account of the origins of data science, a compelling political narrative of America in the Sixties, a poignant collective biography of a generation of flawed men' David Kynaston\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003e'If Then is simultaneously gripping and absolutely terrifying' Amanda Foreman\u003c\/b\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eLepore is a brilliant writer. It's a dream to read.\u003c\/b\u003e -- Diane Coyle\u003cbr\u003e\u003cb\u003eIf you're looking for beautiful writing and love history ... this is a lovely read that takes you through a history of American politics and campaigning, cold war intrigue and artificial intelligence. \u003c\/b\u003e * Financial Times *\u003cbr\u003e\u003cb\u003eJill Lepore is the pre-eminent historian of forgotten tales from America's past that throw startling light on the present.  This brilliant book illuminates the future too.  The story of the original data science hucksters of the 1960s is hilarious, scathing and sobering - what you might get if you crossed \u003ci\u003eMad Men\u003c\/i\u003e with Theranos.\u003c\/b\u003e -- David Runciman\u003cbr\u003e\u003cb\u003eFascinating.\u003c\/b\u003e * New York Times Book Review *\u003cbr\u003e\u003cb\u003eA person can't help but feel inspired by the riveting intelligence and joyful curiosity of Jill Lepore. Knowing that there is a mind like hers in the world is a hope-inducing thing.\u003c\/b\u003e -- George Saunders\u003cbr\u003e\u003cb\u003eJill Lepore writes history like a poet. In \u003ci\u003eIf Then\u003c\/i\u003e she yet again binds lyrical story telling to meticulous archival research to tell a gigantic story from our past. She builds our present, and makes it feel so familiar and yet so contingent. \u003c\/b\u003e -- Dan Snow\u003cbr\u003e\u003cb\u003eTwo things make this tale worth reading. One is Lepore's brisk and confident depiction of the individuals involved...the other is her exploration of the growing power of computers to accumulate and analyse data, bringing marketing and politics into ever closer union.\u003c\/b\u003e -- Frances Cairncross * The Literary Review *\u003cbr\u003e\u003cb\u003eBeautifully written and intellectually rigorous account of the origins of the science of predictive analytics and behavioral data science in the cold war era.\u003c\/b\u003e * Financial Times *\u003cbr\u003e\u003cb\u003eFascinating.\u003c\/b\u003e -- Amol Rajan * Start the Week *\u003cbr\u003e\u003cb\u003eEverything Lepore writes is distinguished by intelligence, eloquence, and fresh insight. \u003ci\u003eIf Then\u003c\/i\u003e is that, and even more: It's absolutely fascinating, excavating a piece of little-known American corporate history that reveals a huge amount about the way we live today and the companies that define the modern era. \u003c\/b\u003e -- Susan Orlean\u003cbr\u003e\u003cb\u003eA wonderfully written history of long-forgotten computer group Simulmatics. \u003c\/b\u003e * Financial Times *","brand":"John Murray Press","offers":[{"title":"Default Title","offer_id":48740254417239,"sku":"9781529386158","price":18.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781529386158.jpg?v=1720054231"},{"product_id":"if-then-how-one-data-company-invented-the-future-9781529386172","title":"If Then: How One Data Company Invented the Future","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eRadio 4's Book of the Week\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eA Financial Times Book of the Year\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eShortlisted for the 2020 Financial Times \/ McKinsey Business Book of the Year\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eLonglisted for the National Book Award \u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003e'The story of the original data science hucksters of the 1960s is hilarious, scathing and sobering - what you might get if you crossed Mad Men with Theranos' David Runciman\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eThe Simulmatics Corporation, founded in 1959, mined data, targeted voters, accelerated news, manipulated consumers, destabilized politics, and disordered knowledge--decades before Facebook, Google, Amazon, and Cambridge Analytica.  Silicon Valley likes to imagine it has no past but the scientists of Simulmatics are the long-dead grandfathers of Mark Zuckerberg and Elon Musk. \u003cbr\u003e\u003cbr\u003eBorrowing from psychological warfare, they used computers to predict and direct human behavior, deploying their \"People Machine\" from New York, Cambridge, and Saigon for clients that included John Kennedy's presidential campaign, the \u003ci\u003eNew York Times\u003c\/i\u003e, Young \u0026amp; Rubicam, and, during the Vietnam War, the Department of Defence. \u003cbr\u003e\u003cbr\u003eIn \u003ci\u003eIf Then\u003c\/i\u003e, distinguished Harvard historian and \u003ci\u003eNew Yorker \u003c\/i\u003estaff writer, Jill Lepore, unearths from the archives the almost unbelievable story of this long-vanished corporation, and of the women hidden behind it. In the 1950s and 1960s, Lepore argues, Simulmatics invented the future by building the machine in which the world now finds itself trapped and tormented, algorithm by algorithm.\u003cbr\u003e\u003cbr\u003e\u003cb\u003e'A person can't help but feel inspired by the riveting intelligence and joyful curiosity of Jill Lepore. Knowing that there is a mind like hers in the world is a hope-inducing thing' George Saunders, Man Booker Prize-winning author of \u003ci\u003eLincoln in the Bardo\u003c\/i\u003e\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003e'An authoritative account of the origins of data science, a compelling political narrative of America in the Sixties, a poignant collective biography of a generation of flawed men' David Kynaston\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003e'If Then is simultaneously gripping and absolutely terrifying' Amanda Foreman\u003c\/b\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eLepore is a brilliant writer. It's a dream to read.\u003c\/b\u003e -- Diane Coyle\u003cbr\u003e\u003cb\u003eIf you're looking for beautiful writing and love history ... this is a lovely read that takes you through a history of American politics and campaigning, cold war intrigue and artificial intelligence. \u003c\/b\u003e * Financial Times *\u003cbr\u003e\u003cb\u003eJill Lepore is the pre-eminent historian of forgotten tales from America's past that throw startling light on the present.  This brilliant book illuminates the future too.  The story of the original data science hucksters of the 1960s is hilarious, scathing and sobering - what you might get if you crossed \u003ci\u003eMad Men\u003c\/i\u003e with Theranos.\u003c\/b\u003e -- David Runciman\u003cbr\u003e\u003cb\u003eFascinating.\u003c\/b\u003e * New York Times Book Review *\u003cbr\u003e\u003cb\u003eA person can't help but feel inspired by the riveting intelligence and joyful curiosity of Jill Lepore. Knowing that there is a mind like hers in the world is a hope-inducing thing.\u003c\/b\u003e -- George Saunders\u003cbr\u003e\u003cb\u003eJill Lepore writes history like a poet. In \u003ci\u003eIf Then\u003c\/i\u003e she yet again binds lyrical story telling to meticulous archival research to tell a gigantic story from our past. She builds our present, and makes it feel so familiar and yet so contingent. \u003c\/b\u003e -- Dan Snow\u003cbr\u003e\u003cb\u003eTwo things make this tale worth reading. One is Lepore's brisk and confident depiction of the individuals involved...the other is her exploration of the growing power of computers to accumulate and analyse data, bringing marketing and politics into ever closer union.\u003c\/b\u003e -- Frances Cairncross * The Literary Review *\u003cbr\u003e\u003cb\u003eBeautifully written and intellectually rigorous account of the origins of the science of predictive analytics and behavioral data science in the cold war era.\u003c\/b\u003e * Financial Times *\u003cbr\u003e\u003cb\u003eFascinating.\u003c\/b\u003e -- Amol Rajan * Start the Week *\u003cbr\u003e\u003cb\u003eEverything Lepore writes is distinguished by intelligence, eloquence, and fresh insight. \u003ci\u003eIf Then\u003c\/i\u003e is that, and even more: It's absolutely fascinating, excavating a piece of little-known American corporate history that reveals a huge amount about the way we live today and the companies that define the modern era. \u003c\/b\u003e -- Susan Orlean\u003cbr\u003e\u003cb\u003eA wonderfully written history of long-forgotten computer group Simulmatics. \u003c\/b\u003e * Financial Times *","brand":"John Murray Press","offers":[{"title":"Default Title","offer_id":48740254482775,"sku":"9781529386172","price":10.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781529386172.jpg?v=1720054230"},{"product_id":"core-data-analysis-summarization-correlation-and-visualization-9783030002701","title":"Core Data Analysis: Summarization, Correlation,","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis text examines the goals of data analysis with respect to enhancing knowledge, and identifies data summarization and correlation analysis as the core issues. Data summarization, both quantitative and categorical, is treated within the encoder-decoder paradigm bringing forward a number of mathematically supported insights into the methods and relations between them. Two Chapters describe methods for categorical summarization: partitioning, divisive clustering and separate cluster finding and another explain the methods for quantitative summarization, Principal Component Analysis and PageRank. \u003c\/p\u003e\u003cp\u003eFeatures:\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e·        An in-depth presentation of K-means partitioning including a corresponding Pythagorean decomposition of the data scatter. \u003c\/p\u003e\u003cp\u003e·        Advice regarding such issues as clustering of categorical and mixed scale data, similarity and network data, interpretation aids, anomalous clusters, the number of clusters, etc.\u003c\/p\u003e\u003cp\u003e·        Thorough attention to data-driven modelling including a number of mathematically stated relations between statistical and geometrical concepts including those between goodness-of-fit criteria for decision trees and data standardization, similarity and consensus clustering, modularity clustering and uniform partitioning.\u003c\/p\u003e\u003cp\u003eNew edition highlights: \u003c\/p\u003e\u003cp\u003e·        Inclusion of ranking issues such as Google PageRank, linear stratification and tied rankings median, consensus clustering, semi-average clustering, one-cluster clustering\u003c\/p\u003e\u003cp\u003e·        Restructured to make the logics more straightforward and sections self-contained\u003c\/p\u003e\u003cp\u003e\u003ci\u003eCore Data Analysis: Summarization, Correlation and Visualization\u003c\/i\u003e is aimed at those who are eager to participate in developing the field as well as appealing to novices and practitioners. \u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“This book provides a clear overview of the data analysis process, the different types of statistical techniques employed for data analysis, and their role and purpose. … There is good use of a variety of examples to demonstrate how the different techniques are applied in practice. The book’s main purpose would be as a textbook for undergraduate students, or a reference book for data analysts.” (Mark Taylor, Computing Reviews, May 5, 2022)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743020757335,"sku":"9783030002701","price":54.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"federated-learning-for-iot-applications-9783030855581","title":"Federated Learning for IoT Applications","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book presents how federated learning helps to understand and learn from user activity in Internet of Things (IoT) applications while protecting user privacy. The authors first show how federated learning provides a unique way to build personalized models using data without intruding on users’ privacy. The authors then provide a comprehensive survey of state-of-the-art research on federated learning, giving the reader a general overview of the field. The book also investigates how a personalized federated learning framework is needed in cloud-edge architecture as well as in wireless-edge architecture for intelligent IoT applications. To cope with the heterogeneity issues in IoT environments, the book investigates emerging personalized federated learning methods that are able to mitigate the negative effects caused by heterogeneities in different aspects. The book provides case studies of IoT based human activity recognition to demonstrate the effectiveness of personalized federated learning for intelligent IoT applications, as well as multiple controller design and system analysis tools including model predictive control, linear matrix inequalities, optimal control, etc. This unique and complete co-design framework will benefit researchers, graduate students and engineers in the fields of control theory and engineering. \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eChapter 1. Introduction to Federated Learning.- Chapter 2. Federated Learning for IoT Devices.- Chapter 3. Personalized Federated Learning.- Chapter 4. Federated Learning for an IoT Application.- Chapter 5. Some observations on the behaviour of Federated Learning.- Chapter 6. Federated Learning with Cooperating Devices: A Consensus Approach.- Chapter 7. A prospective study of federated machine learning in medical image fusion.- Chapter 8. Communication-Efficient Federated Learning in Wireless-Edge Architecture.- Chapter 9. Towards Ubiquitous AI in 6G with Federated Learning.- Chapter 10. Federated Learning using Tensor Flow.- Chapter 11. Cyber Security and privacy of Connected and Automated Vehicles (CAVs) based Federated Learning:  Challenges, Opportunities and Open Issues.- Chapter 12. Security Issues \u0026amp; Solutions for Healthcare Informatics.- Chapter 13. Federated Learning: Challenges, Methods, and Future Directions.- Chapter 14. Quantum Federated Learning for Wireless Communications.- Chapter 15. Federated machine learning with data mining in health care.- Chapter 16.  Federated Learning for data mining in Healthcare.\u003cp\u003e\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743054278999,"sku":"9783030855581","price":94.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"advanced-data-mining-and-applications-17th-international-conference-adma-2021-sydney-nsw-australia-february-2-4-2022-proceedings-part-ii-9783030954079","title":"Advanced Data Mining and Applications: 17th","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book constitutes the proceedings of the 17th International Conference on Advanced Data Mining and Applications, ADMA 2021, held in Sydney, Australia in February 2022.*\u003c\/p\u003e\u003cp\u003eThe 26 full papers presented together with 35 short papers were carefully reviewed and selected from 116 submissions. The papers were organized in topical sections in Part II named: Pattern mining; Graph mining; Text mining; Multimedia and time series data mining; and Classification, clustering and recommendation.\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003e* The conference was originally planned for December 2021, but was postponed to 2022.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743061193047,"sku":"9783030954079","price":64.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"machine-learning-for-text-9783030966225","title":"Machine Learning for Text","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis second edition textbook covers a coherently organized framework for text analytics, which integrates material drawn from the intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning methods. The chapters of this book span three broad categories:1. Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for text analytics such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis.\u003cp\u003e\u003c\/p\u003e\u003cp\u003e2. Domain-sensitive learning and information retrieval: Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. \u003c\/p\u003e3. Natural language processing: Chapters 10 through 16 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, transformers, pre-trained language models, text summarization, information extraction, knowledge graphs, question answering, opinion mining, text segmentation, and event detection. \u003cp\u003e\u003c\/p\u003e\u003cp\u003eCompared to the first edition, this second edition textbook (which targets mostly advanced level students majoring in computer science and math) has substantially more material on deep learning and natural language processing. Significant focus is placed on topics like transformers, pre-trained language models, knowledge graphs, and question answering.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1 An Introduction to Text Analytics.- 2 Text Preparation and Similarity Computation.- 3 Matrix Factorization and Topic Modeling.- 4 Text Clustering.- 5 Text Classification: Basic Models.- 6 Linear Models for Classification and Regression.- 7 Classifier Performance and Evaluation.- 8 Joint Text Mining with Heterogeneous Data.- 9 Information Retrieval and Search Engines.- 10 Language Modeling and Deep Learning.- 11 Attention Mechanisms and Transformers.- 12 Text Summarization.- 13 Information Extraction and Knowledge Graphs.- 14 Question Answering.- 15 Opinion Mining and Sentiment Analysis.- 16 Text Segmentation and Event Detection.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743062110551,"sku":"9783030966225","price":51.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030966225.jpg?v=1720063944"},{"product_id":"the-data-science-design-manual-9783319554433","title":"The Data Science Design Manual","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis engaging and clearly written textbook\/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data.\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e\u003ci\u003eThe Data Science Design Manual\u003c\/i\u003e\u003c\/b\u003e is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles.\u003c\/p\u003e  This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well.\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eAdditional learning tools:\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eContains “War Stories,” offering perspectives on how data science applies in the real world\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eIncludes “Homework Problems,” providing a wide range of exercises and projects for self-study\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eProvides a complete set of lecture slides and online video lectures at www.data-manual.com\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eProvides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eRecommends exciting “Kaggle Challenges” from the online platform Kaggle\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eHighlights “False Starts,” revealing the subtle reasons why certain approaches fail\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eOffers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)\u003cbr\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003e“The book is more than a typical manual. In fact, the author himself designates it as a textbook for an introductory course on data science. The chapters are richly equipped with exercises. The topics are always explained starting with a proper motivation and continuing with practical examples. This is perhaps the most outstanding feature of the book. It can serve as a regular textbook for an academic course. In fact, I should like to recommend it exactly for this purpose. On the other hand, it provides a wealth of material for people from industry, such as software engineers, and can serve as a manual for them to accomplish data science tasks. It should be noted that the book is not just a text, but a much more complex product, including a full set of lecture slides available online as well as a solutions wiki.” (P. Navrat, Computing Reviews, February, 23, 2018)\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e  ​\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eWhat is Data Science?\u003cp\u003e\u003c\/p\u003e  \u003cp\u003eMathematical Preliminaries\u003c\/p\u003e  \u003cp\u003eData Munging\u003c\/p\u003e  \u003cp\u003eScores and Rankings\u003c\/p\u003e  \u003cp\u003eStatistical Analysis\u003c\/p\u003e  \u003cp\u003eVisualizing Data\u003c\/p\u003e  \u003cp\u003eMathematical Models\u003c\/p\u003e  \u003cp\u003eLinear Algebra\u003c\/p\u003e  \u003cp\u003eLinear and Logistic Regression\u003c\/p\u003e  \u003cp\u003eDistance and Network Methods\u003c\/p\u003e  \u003cp\u003eMachine Learning\u003c\/p\u003e  \u003cp\u003eBig Data: Achieving Scale\u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743098220887,"sku":"9783319554433","price":45.55,"currency_code":"GBP","in_stock":true}]},{"product_id":"algorithmic-intelligence-towards-an-algorithmic-foundation-for-artificial-intelligence-9783319655956","title":"Algorithmic Intelligence: Towards an Algorithmic","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIn this book the author argues that the basis of what we consider computer intelligence has algorithmic roots, and he presents this with a holistic view, showing examples and explaining approaches that encompass theoretical computer science and machine learning via engineered algorithmic solutions.\u003c\/p\u003e\u003cp\u003ePart I of the book introduces the basics. The author starts with a hands-on programming primer for solving combinatorial problems, with an emphasis on recursive solutions. The other chapters in the first part of the book explain shortest paths, sorting, deep learning, and Monte Carlo search. \u003c\/p\u003e\u003cp\u003eA key function of computational tools is processing Big Data efficiently, and the chapters in Part II of the book examine traditional graph problems such as finding cliques, colorings, independent sets, vertex covers, and hitting sets, and the subsequent chapters cover multimedia, network, image, and navigation data. \u003c\/p\u003e\u003cp\u003eThe highly topical research areas detailed in Part III are machine learning, problem solving, action planning, general game playing, multiagent systems, and recommendation and configuration. \u003c\/p\u003e\u003cp\u003eFinally, in Part IV the author uses application areas such as model checking, computational biology, logistics, additive manufacturing, robot motion planning, and industrial production to explain how the techniques described may be exploited in modern settings.\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eThe book is supported with a comprehensive index and references, and it will be of value to researchers, practitioners, and students in the areas of artificial intelligence and computational intelligence.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface.- Towards a Characterization.- Part I, Basics.- 1. Programming Primer.- 2. Shortest Paths.- 3. Sorting.- 4. Deep Learning.- 5. Monte-Carlo Search.- Part II, Big Data.- 6. Graph data.- 7. Multimedia Data.- 8. Network Data.- 9. Image Data.- 10. Navigation Data.- Part III, Research Areas.- 11. Machine Learning.- 12. Problem Solving.- 13. Card Game Playing.- 14. Action Planning.- 15. General Game Playing.- 16. Multiagent Systems.- 17. Recommendation and Configuration Part IV, Applications.- 18. Adversarial Planning.- 19. Model Checking.- 20. Computational Biology.- 21. Logistics.- 22. Additive Manufacturing.- 23. Robot Motion Planning.- 24. Industrial Production.- 25. Further Application Areas. - Index and References\u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743101235543,"sku":"9783319655956","price":170.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783319655956.jpg?v=1720064115"},{"product_id":"data-matching-concepts-and-techniques-for-record-linkage-entity-resolution-and-duplicate-detection-9783642430015","title":"Data Matching: Concepts and Techniques for Record","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eData matching (also known as record or data linkage, entity resolution, object identification, or field matching) is the task of identifying, matching and merging records that correspond to the same entities from several databases or even within one database. Based on research in various domains including applied statistics, health informatics, data mining, machine learning, artificial intelligence, database management, and digital libraries, significant advances have been achieved over the last decade in all aspects of the data matching process, especially on how to improve the accuracy of data matching, and its scalability to large databases.\u003c\/p\u003e\u003cp\u003ePeter Christen’s book is divided into three parts: Part I, “Overview”, introduces the subject by presenting several sample applications and their special challenges, as well as a general overview of a generic data matching process. Part II, “Steps of the Data Matching Process”, then details its main steps like pre-processing, indexing, field and record comparison, classification, and quality evaluation. Lastly, part III, “Further Topics”, deals with specific aspects like privacy, real-time matching, or matching unstructured data. Finally, it briefly describes the main features of many research and open source systems available today.\u003c\/p\u003eBy providing the reader with a broad range of data matching concepts and techniques and touching on all aspects of the data matching process, this book helps researchers as well as students specializing in data quality or data matching aspects to familiarize themselves with recent research advances and to identify open research challenges in the area of data matching. To this end, each chapter of the book includes a final section that provides pointers to further background and research material. Practitioners will better understand the current state of the art in data matching as well as the internal workings and limitations of current systems. Especially, they will learn that it is often not feasible to simply implement an existing off-the-shelf data matching system without substantial adaption and customization. Such practical considerations are discussed for each of the major steps in the data matching process.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003ci\u003e\"The book is very well organized and exceptionally well written. Because of the depth, amount, and quality of the material that is covered, I would expect this book to be one of the standard references in future years.\"\u003c\/i\u003e William E. Winkler, U.S. Bureau of the Census, Washington, DC, USA\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePart I Overview.- Introduction.- The Data Matching Process.- Part II Steps of the Data Matching Process.- Data Pre-Processing.- Indexing.- Field and Record Comparison.- Classification.- Evaluation of Matching Quality and Complexity.- Part III Further Topics.- Privacy Aspects of Data Matching.- Further Topics and Research Directions.- Data Matching Systems.\u003c\/p\u003e","brand":"Springer-Verlag Berlin and Heidelberg GmbH \u0026 Co. KG","offers":[{"title":"Default Title","offer_id":48743135838551,"sku":"9783642430015","price":113.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"big-data-im-gesundheitswesen-kompakt-konzepte-losungen-visionen-9783658210953","title":"Big Data im Gesundheitswesen kompakt: Konzepte,","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eDas kompakte Fachbuch gibt einen Überblick über die Möglichkeiten von „Big Data“ im Gesundheitswesen und beschreibt anhand von ausgewählten Szenarien mögliche Einsatzgebiete.\u003c\/p\u003e\u003cp\u003eDie Autoren erläutern zentrale Systemkomponenten und IT-Standards und thematisieren anhand wichtiger Daten des Gesundheitswesens die Notwendigkeit der Strukturierung und Modellierung von Daten. Das Buch gibt Hinweise wie Geschäftsprozesse im Gesundheitswesen dokumentiert, analysiert und verbessert werden können. Anwendungsszenarien, wie die Datenanalysen für Krankenhäuser, Labore, Versicherungen und die Pharmaindustrie, zeigen die praktische Relevanz des Themas. Aber auch rechtliche und ethische Aspekte werden inhaltlich angeschnitten.\u003c\/p\u003e\u003cp\u003eEin Buch für Entscheider in der medizinischen Leitung und Verwaltung von Krankenhäusern, Fachleute sowie niedergelassene Ärzte und Apotheker, aber auch Personen in Ausbildung und Studium im Gesundheitswesen. \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eBig-Data-Analytics im Gesundheitswesen - Medizin - Verwaltung - Forschung: Anwendungsgebiete für Big-Data-Analytics - Gesetzliche Rahmenbedingungen und Big-Data-Ethik\u003c\/p\u003e","brand":"Springer Fachmedien Wiesbaden","offers":[{"title":"Default Title","offer_id":48743137837399,"sku":"9783658210953","price":13.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783658210953.jpg?v=1720064275"},{"product_id":"self-service-analytics-with-power-bi-learn-how-to-build-an-end-to-end-analytics-solution-in-power-bi-9789355518200","title":"Self-Service Analytics with Power BI: Learn how","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"BPB Publications","offers":[{"title":"Default Title","offer_id":48743244923223,"sku":"9789355518200","price":26.59,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9789355518200.jpg?v=1720064754"},{"product_id":"everybody-lies-9780062390851","title":"Everybody Lies","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003e New York Times Bestseller\u003c\/b\u003e\u003cp\u003e\u003cb\u003eForeword by Steven Pinker,...\u003c\/b\u003e\u003c\/p\u003e","brand":"HarperCollins Publishers Inc","offers":[{"title":"Default Title","offer_id":48864080068951,"sku":"9780062390851","price":21.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780062390851.jpg?v=1722270297"},{"product_id":"analyzing-social-media-networks-with-nodexl-9780128177563","title":"Analyzing Social Media Networks with NodeXL","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart I. Getting Started with Analyzing Social Media Networks 1. Introduction to Social Media and Social Networks 2. Social Media: New Technologies of Collaboration 3. Social Network Analysis: Measuring, Mapping, and Modeling Collections of Connections   Part II. NodeXL Tutorial: Learning by Doing 4. Installation, Orientation, and Layout 5. Labeling and Visual Attributes 6. Calculating and Visualizing Network Metrics 7. Grouping and Filtering 8. Semantic Networks   Part III. Social Media Network Analysis Case Studies 9. Email: The Lifeblood of Modern Communication 10. Thread Networks: Mapping Message Boards and Email Lists 11. Twitter: Information Flows, Influencers, and Organic Communities 12. Facebook: Public Pages and Inter-Organizational Networks 13. YouTube: Exploring Video Networks 14. Wiki Networks: Connections of Culture and Collaboration","brand":"Elsevier Science \u0026 Technology","offers":[{"title":"Default Title","offer_id":48864165593431,"sku":"9780128177563","price":37.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780128177563.jpg?v=1722270698"},{"product_id":"pandas-for-everyone-9780137891153","title":"Pandas for Everyone","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cstrong\u003eDaniel Chen\u003c\/strong\u003e is a graduate student in the Interdisciplinary PhD program in Genetics, Bioinformatics \u0026amp; Computational Biology (GBCB) at Virginia Polytechnic Institute and State University (Virginia Tech). He is involved with Software Carpentry as an instructor, Mentoring Committee Member, and currently serves as the Assessment Committee Chair. He completed his Masters in Public Health at Columbia University Mailman School of Public Health in Epidemiology with a certificate in Advanced Epidemiology and currently extending his Master's thesis work in the Social and Decision Analytics Laboratory under the Virginia Bioinformatics Institute on attitude diffusion in social networks.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cem\u003eForeword by Anne M. Brown\u003c\/em\u003e     xxiii\u003c\/p\u003e \u003cp\u003e\u003cem\u003eForeword by Jared Lander\u003c\/em\u003e     xxv\u003c\/p\u003e \u003cp\u003e\u003cem\u003ePreface\u003c\/em\u003e     xxvii\u003c\/p\u003e \u003cp\u003e\u003cem\u003eChanges in the Second Edition\u003c\/em\u003e     xxxix\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003ePart I: Introduction\u003c\/strong\u003e    1\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 1. Pandas DataFrame Basics\u003c\/strong\u003e     3\u003c\/p\u003e \u003cp\u003e       Learning Objectives      3\u003c\/p\u003e \u003cp\u003e       1.1 Introduction      3\u003c\/p\u003e \u003cp\u003e       1.2 Load Your First Data Set      4\u003c\/p\u003e \u003cp\u003e       1.3 Look at Columns, Rows, and Cells      6\u003c\/p\u003e \u003cp\u003e       1.4 Grouped and Aggregated Calculations      23\u003c\/p\u003e \u003cp\u003e       1.5 Basic Plot      27\u003c\/p\u003e \u003cp\u003e       Conclusion      28\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 2. Pandas Data Structures Basics\u003c\/strong\u003e      31\u003c\/p\u003e \u003cp\u003e       Learning Objectives      31\u003c\/p\u003e \u003cp\u003e       2.1 Create Your Own Data      31\u003c\/p\u003e \u003cp\u003e       2.2 The Series      33\u003c\/p\u003e \u003cp\u003e       2.3 The DataFrame      42\u003c\/p\u003e \u003cp\u003e       2.4 Making Changes to Series and DataFrames      45\u003c\/p\u003e \u003cp\u003e       2.5 Exporting and Importing Data      52\u003c\/p\u003e \u003cp\u003e       Conclusion      63\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 3. Plotting Basics\u003c\/strong\u003e      65\u003c\/p\u003e \u003cp\u003e       Learning Objectives      65\u003c\/p\u003e \u003cp\u003e       3.1 Why Visualize Data?       65\u003c\/p\u003e \u003cp\u003e       3.2 Matplotlib Basics      66\u003c\/p\u003e \u003cp\u003e       3.3 Statistical Graphics Using matplotlib      72\u003c\/p\u003e \u003cp\u003e       3.4 Seaborn      78\u003c\/p\u003e \u003cp\u003e       3.5 Pandas Plotting Method      111\u003c\/p\u003e \u003cp\u003e       Conclusion      115\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 4. Tidy Data\u003c\/strong\u003e      117\u003c\/p\u003e \u003cp\u003e       Learning Objectives      117\u003c\/p\u003e \u003cp\u003e       Note About This Chapter       117\u003c\/p\u003e \u003cp\u003e       4.1 Columns Contain Values, Not Variables      118\u003c\/p\u003e \u003cp\u003e       4.2 Columns Contain Multiple Variables      122\u003c\/p\u003e \u003cp\u003e       4.3 Variables in Both Rows and Columns      126\u003c\/p\u003e \u003cp\u003e       Conclusion      129\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 5. Apply Functions\u003c\/strong\u003e      131\u003c\/p\u003e \u003cp\u003e       Learning Objectives      131\u003c\/p\u003e \u003cp\u003e       Note About This Chapter      131\u003c\/p\u003e \u003cp\u003e       5.1 Primer on Functions      131\u003c\/p\u003e \u003cp\u003e       5.2 Apply (Basics)       133\u003c\/p\u003e \u003cp\u003e       5.3 Vectorized Functions      138\u003c\/p\u003e \u003cp\u003e       5.4 Lambda Functions (Anonymous Functions)       141\u003c\/p\u003e \u003cp\u003e       Conclusion      142\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003ePart II: Data Processing\u003c\/strong\u003e     143\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 6. Data Assembly\u003c\/strong\u003e      145\u003c\/p\u003e \u003cp\u003e       Learning Objectives      145\u003c\/p\u003e \u003cp\u003e       6.1 Combine Data Sets      145\u003c\/p\u003e \u003cp\u003e       6.2 Concatenation      146\u003c\/p\u003e \u003cp\u003e       6.3 Observational Units Across Multiple Tables      154\u003c\/p\u003e \u003cp\u003e       6.4 Merge Multiple Data Sets      160\u003c\/p\u003e \u003cp\u003e       Conclusion      167\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 7. Data Normalization\u003c\/strong\u003e      169\u003c\/p\u003e \u003cp\u003e       Learning Objectives      169\u003c\/p\u003e \u003cp\u003e       7.1 Multiple Observational Units in a Table (Normalization)     169\u003c\/p\u003e \u003cp\u003e       Conclusion      173\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 8. Groupby Operations: Split-Apply-Combine\u003c\/strong\u003e      175\u003c\/p\u003e \u003cp\u003e       Learning Objectives      175\u003c\/p\u003e \u003cp\u003e       8.1 Aggregate      176\u003c\/p\u003e \u003cp\u003e       8.2 Transform      184\u003c\/p\u003e \u003cp\u003e       8.3 Filter      188\u003c\/p\u003e \u003cp\u003e       8.4 The pandas.core.groupby.DataFrameGroupBy object      190\u003c\/p\u003e \u003cp\u003e       8.5 Working with a MultiIndex      195\u003c\/p\u003e \u003cp\u003e       Conclusion      199\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003ePart III: Data Types\u003c\/strong\u003e    203\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 9. Missing Data\u003c\/strong\u003e      203\u003c\/p\u003e \u003cp\u003e       Learning Objectives      203\u003c\/p\u003e \u003cp\u003e       9.1 What Is a NaN Value?       203\u003c\/p\u003e \u003cp\u003e       9.2 Where Do Missing Values Come From?       205\u003c\/p\u003e \u003cp\u003e       9.3 Working with Missing Data      210\u003c\/p\u003e \u003cp\u003e       9.4 Pandas Built-In NA Missing      216\u003c\/p\u003e \u003cp\u003e       Conclusion      218\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 10. Data Types\u003c\/strong\u003e      219\u003c\/p\u003e \u003cp\u003e       Learning Objectives      219\u003c\/p\u003e \u003cp\u003e       10.1 Data Types      219\u003c\/p\u003e \u003cp\u003e       10.2 Converting Types      220\u003c\/p\u003e \u003cp\u003e       10.3 Categorical Data      225\u003c\/p\u003e \u003cp\u003e       Conclusion      227\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 11. Strings and Text Data\u003c\/strong\u003e      229\u003c\/p\u003e \u003cp\u003e       Introduction      229\u003c\/p\u003e \u003cp\u003e       Learning Objectives      229\u003c\/p\u003e \u003cp\u003e       11.1 Strings      229\u003c\/p\u003e \u003cp\u003e       11.2 String Methods      233\u003c\/p\u003e \u003cp\u003e       11.3 More String Methods      234\u003c\/p\u003e \u003cp\u003e       11.4 String Formatting (F-Strings)       236\u003c\/p\u003e \u003cp\u003e       11.5 Regular Expressions (RegEx)      239\u003c\/p\u003e \u003cp\u003e       11.6 The regex Library      247\u003c\/p\u003e \u003cp\u003e       Conclusion      247\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 12. Dates and Times \u003c\/strong\u003e     249\u003c\/p\u003e \u003cp\u003e       Learning Objectives      249\u003c\/p\u003e \u003cp\u003e       12.1 Python's datetime Object      249\u003c\/p\u003e \u003cp\u003e       12.2 Converting to datetime      250\u003c\/p\u003e \u003cp\u003e       12.3 Loading Data That Include Dates      253\u003c\/p\u003e \u003cp\u003e       12.4 Extracting Date Components      254\u003c\/p\u003e \u003cp\u003e       12.5 Date Calculations and Timedeltas      257\u003c\/p\u003e \u003cp\u003e       12.6 Datetime Methods      259\u003c\/p\u003e \u003cp\u003e       12.7 Getting Stock Data      261\u003c\/p\u003e \u003cp\u003e       12.8 Subsetting Data Based on Dates      263\u003c\/p\u003e \u003cp\u003e       12.9 Date Ranges      266\u003c\/p\u003e \u003cp\u003e       12.10 Shifting Values      270\u003c\/p\u003e \u003cp\u003e       12.11 Resampling      276\u003c\/p\u003e \u003cp\u003e       12.12 Time Zones      278\u003c\/p\u003e \u003cp\u003e       12.13 Arrow for Better Dates and Times      280\u003c\/p\u003e \u003cp\u003e       Conclusion      280\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003ePart IV: Data Modeling\u003c\/strong\u003e    281\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 13. Linear Regression (Continuous Outcome Variable)\u003c\/strong\u003e      283\u003c\/p\u003e \u003cp\u003e       13.1 Simple Linear Regression      283\u003c\/p\u003e \u003cp\u003e       13.2 Multiple Regression      287\u003c\/p\u003e \u003cp\u003e       13.3 Models with Categorical Variables      289\u003c\/p\u003e \u003cp\u003e       13.4 One-Hot Encoding in scikit-learn with Transformer Pipelines      294\u003c\/p\u003e \u003cp\u003e       Conclusion      296\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 14. Generalized Linear Models\u003c\/strong\u003e      297\u003c\/p\u003e \u003cp\u003e       About This Chapter      297\u003c\/p\u003e \u003cp\u003e       14.1 Logistic Regression (Binary Outcome Variable)       297\u003c\/p\u003e \u003cp\u003e       14.2 Poisson Regression (Count Outcome Variable)       304\u003c\/p\u003e \u003cp\u003e       14.3 More Generalized Linear Models      308\u003c\/p\u003e \u003cp\u003e       Conclusion      309\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 15. Survival Analysis\u003c\/strong\u003e      311\u003c\/p\u003e \u003cp\u003e       15.1 Survival Data      311\u003c\/p\u003e \u003cp\u003e       15.2 Kaplan Meier Curves      312\u003c\/p\u003e \u003cp\u003e       15.3 Cox Proportional Hazard Model      314\u003c\/p\u003e \u003cp\u003e       Conclusion      317\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 16. Model Diagnostics\u003c\/strong\u003e      319\u003c\/p\u003e \u003cp\u003e       16.1 Residuals      319\u003c\/p\u003e \u003cp\u003e       16.2 Comparing Multiple Models      324\u003c\/p\u003e \u003cp\u003e       16.3 k-Fold Cross-Validation      329\u003c\/p\u003e \u003cp\u003e       Conclusion      334\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 17. Regularization\u003c\/strong\u003e      335\u003c\/p\u003e \u003cp\u003e       17.1 Why Regularize?       335\u003c\/p\u003e \u003cp\u003e       17.2 LASSO Regression      337\u003c\/p\u003e \u003cp\u003e       17.3 Ridge Regression      338\u003c\/p\u003e \u003cp\u003e       17.4 Elastic Net      340\u003c\/p\u003e \u003cp\u003e       17.5 Cross-Validation      341\u003c\/p\u003e \u003cp\u003e       Conclusion      343\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 18. Clustering\u003c\/strong\u003e      345\u003c\/p\u003e \u003cp\u003e       18.1 k-Means      345\u003c\/p\u003e \u003cp\u003e       18.2 Hierarchical Clustering      351\u003c\/p\u003e \u003cp\u003e       Conclusion     356\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003ePart V. Conclusion\u003c\/strong\u003e    357\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 19. Life Outside of Pandas\u003c\/strong\u003e      359\u003c\/p\u003e \u003cp\u003e       19.1 The (Scientific) Computing Stack      359\u003c\/p\u003e \u003cp\u003e       19.2 Performance      360\u003c\/p\u003e \u003cp\u003e       19.3 Dask      360\u003c\/p\u003e \u003cp\u003e       19.4 Siuba      360\u003c\/p\u003e \u003cp\u003e       19.5 Ibis      361\u003c\/p\u003e \u003cp\u003e       19.6 Polars      361\u003c\/p\u003e \u003cp\u003e       19.7 PyJanitor      361\u003c\/p\u003e \u003cp\u003e       19.8 Pandera      361\u003c\/p\u003e \u003cp\u003e       19.9 Machine Learning      361\u003c\/p\u003e \u003cp\u003e       19.10 Publishing      362\u003c\/p\u003e \u003cp\u003e       19.11 Dashboards      362\u003c\/p\u003e \u003cp\u003e       Conclusion      362\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 20. It's Dangerous To Go Alone!\u003c\/strong\u003e      363\u003c\/p\u003e \u003cp\u003e       20.1 Local Meetups      363\u003c\/p\u003e \u003cp\u003e       20.2 Conferences      363\u003c\/p\u003e \u003cp\u003e       20.3 The Carpentries      364\u003c\/p\u003e \u003cp\u003e       20.4 Podcasts      364\u003c\/p\u003e \u003cp\u003e       20.5 Other Resources      365\u003c\/p\u003e \u003cp\u003e       Conclusion      365\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eAppendices\u003c\/strong\u003e      367\u003c\/p\u003e \u003cp\u003eA.      Concept Maps      369\u003cbr\u003eB.      Installation and Setup     373\u003cbr\u003eC.      Command Line     377\u003cbr\u003eD.      Project Templates     379\u003cbr\u003eE.      Using Python       381\u003cbr\u003eF.       Working Directories       383\u003cbr\u003eG.      Environments       385\u003cbr\u003eH.      Install Packages       389\u003cbr\u003eI.       Importing Libraries       391\u003cbr\u003eJ.       Code Style       393\u003cbr\u003eK.      Containers: Lists, Tuples, and Dictionaries       395\u003cbr\u003eL.      Slice Values       399\u003cbr\u003eM.     Loops       401\u003cbr\u003eN.     Comprehensions       403\u003cbr\u003eO.     Functions       405\u003cbr\u003eP.      Ranges and Generators       409\u003cbr\u003eQ.     Multiple Assignment       413\u003cbr\u003eR.     Numpy ndarray       415\u003cbr\u003eS.     Classes       417\u003cbr\u003eT.      SettingWithCopyWarning       419\u003cbr\u003eU.     Method Chaining       423\u003cbr\u003eV.      Timing Code       427\u003cbr\u003eW.     String Formatting       429\u003cbr\u003eX.      Conditionals (if-elif-else)        433\u003cbr\u003eY.      New York ACS Logistic Regression Example       435\u003cbr\u003eZ.      Replicating Results in R       443\u003cbr\u003e\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cem\u003eIndex\u003c\/em\u003e      451\u003c\/p\u003e","brand":"Pearson Education (US)","offers":[{"title":"Default Title","offer_id":48864176734551,"sku":"9780137891153","price":34.19,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780137891153.jpg?v=1722270750"},{"product_id":"the-elements-of-statistical-learning-springer-series-in-statistics-9780387848570","title":"The Elements of Statistical Learning Springer","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eOverview of Supervised Learning.- Linear Methods for Regression.- Linear Methods for Classification.- Basis Expansions and Regularization.- Kernel Smoothing Methods.- Model Assessment and Selection.- Model Inference and Averaging.- Additive Models, Trees, and Related Methods.- Boosting and Additive Trees.- Neural Networks.- Support Vector Machines and Flexible Discriminants.- Prototype Methods and Nearest-Neighbors.- Unsupervised Learning.- Random Forests.- Ensemble Learning.- Undirected Graphical Models.- High-Dimensional Problems: p ? N.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eFrom the reviews:\u003c\/p\u003e\u003cp\u003e\"Like the first edition, the current one is a welcome edition to researchers and academicians equally…. Almost all of the chapters are revised.… The Material is nicely reorganized and repackaged, with the general layout being the same as that of the first edition.… If you bought the first edition, I suggest that you buy the second editon for maximum effect, and if you haven’t, then I still strongly recommend you have this book at your desk. Is it a good investment, statistically speaking!\" (Book Review Editor, \u003ci\u003eTechnometrics\u003c\/i\u003e, August 2009, VOL. 51, NO. 3)\u003c\/p\u003e\u003cp\u003eFrom the reviews of the second edition:\u003c\/p\u003e\u003cp\u003e\"This second edition pays tribute to the many developments in recent years in this field, and new material was added to several existing chapters as well as four new chapters … were included. … These additions make this book worthwhile to obtain … . In general this is a well written book which gives a good overview on statistical learning and can be recommended to everyone interested in this field. The book is so comprehensive that it offers material for several courses.\" (Klaus Nordhausen, International Statistical Review, Vol. 77 (3), 2009)\u003c\/p\u003e\u003cp\u003e“The second edition … features about 200 pages of substantial new additions in the form of four new chapters, as well as various complements to existing chapters. … the book may also be of interest to a theoretically inclined reader looking for an entry point to the area and wanting to get an initial understanding of which mathematical issues are relevant in relation to practice. … this is a welcome update to an already fine book, which will surely reinforce its status as a reference.” (Gilles Blanchard, Mathematical Reviews, Issue 2012 d)\u003c\/p\u003e\u003cp\u003e“The book would be ideal for statistics graduate students … . This book really is the standard in the field, referenced in most papers and books on the subject, and it is easy to see why. The book is very well written, with informative graphics on almost every other page. It looks great and inviting. You can flip the book open to any page, read a sentence or two and be hooked for the next hour or so.” (Peter Rabinovitch, The Mathematical Association of America, May, 2012)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eIntroduction.- Overview of supervised learning.- Linear methods for regression.- Linear methods for classification.- Basis expansions and regularization.- Kernel smoothing methods.- Model assessment and selection.- Model inference and averaging.- Additive models, trees, and related methods.- Boosting and additive trees.- Neural networks.- Support vector machines and flexible discriminants.- Prototype methods and nearest-neighbors.- Unsupervised learning.","brand":"Springer-Verlag New York Inc.","offers":[{"title":"Default Title","offer_id":48864540197207,"sku":"9780387848570","price":55.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780387848570.jpg?v=1722272386"},{"product_id":"data-visualization-9780691181622","title":"Data Visualization","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"[Healy’s] prose is engaging and chatty, and the style of instruction is unpretentious and practical . . . This single volume represents an excellent entry point for those wishing to upskill their abilities in data visualization.\"\u003cb\u003e---Paul Cuffe, \u003ci\u003eIEEE Transactions\u003c\/i\u003e\u003c\/b\u003e\u003cbr\u003e\"Undoubtedly, this book is an excellent introduction to an essential tool for anyone who needs to collect and present data.\" * Conservation Biology *","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":48865540669783,"sku":"9780691181622","price":35.7,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780691181622.jpg?v=1722274465"},{"product_id":"python-for-data-analysis-3e-9781098104030","title":"Python for Data Analysis 3e","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eUpdated for Python 3.10 and pandas 1.4, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48866330706263,"sku":"9781098104030","price":47.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781098104030.jpg?v=1722278164"},{"product_id":"snowflake-the-definitive-guide-9781098103828","title":"Snowflake  The Definitive Guide","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eSnowflake's ability to eliminate data silos and run workloads from a single platform creates opportunities to democratize data analytics, allowing users within an organization to make data-driven decisions. This clear, comprehensive guide will show you how to build integrated data applications and develop new revenue streams based on data.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48866330837335,"sku":"9781098103828","price":47.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781098103828.jpg?v=1722278164"},{"product_id":"fundamentals-of-data-engineering-9781098108304","title":"Fundamentals of Data Engineering","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eWith this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48866330935639,"sku":"9781098108304","price":47.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781098108304.jpg?v=1722278165"},{"product_id":"principles-of-database-management-9781107186125","title":"Principles of Database Management","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis comprehensive textbook teaches the fundamentals of database design, modeling, systems, data storage, and the evolving world of data warehousing, governance and more. Written by experienced educators and experts in big data, analytics, data quality, and data integration, it provides an up-to-date approach to database management. This full-color, illustrated text has a balanced theory-practice focus, covering essential topics, from established database technologies to recent trends, like Big Data, NoSQL, and more. Fundamental concepts are supported by real-world examples, query and code walkthroughs, and figures, making it perfect for introductory courses for advanced undergraduates and graduate students in information systems or computer science. These examples are further supported by an online playground with multiple learning environments, including MySQL, MongoDB, Neo4j Cypher, and tree structure visualization. This combined learning approach connects key concepts throughout the text to the important, practical tools to get started in database management.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'Although there have been a series of classical textbooks on database systems, the new dramatic advances call for an updated text covering the latest significant topics, such as big data analytics, No-SQL and much more. Fortunately, this is exactly what this book has to offer. It is highly desirable for training the next generation of data management professionals.' Jian Pei, Simon Fraser University, Canada\u003cbr\u003e'I haven't seen an as up-to-date and comprehensive textbook for Database Management as this one in many years. Principles of Database Management combines a number of classical and recent topics concerning Data Modeling, Relational Databases, Object-Oriented Databases, XML, Distributed Data Management, NoSQL and Big Data in an unprecedented manner. The authors did a great job in stitching these topics into one coherent and compelling story that will serve as an ideal basis for teaching both introductory and advanced courses.' Martin Theobald, University of Luxembourg\u003cbr\u003e'This is a very timely book with outstanding coverage of database topics and excellent treatment of database details. It not only gives very solid discussions of traditional topics like data modeling and relational databases but also contains refreshing contents on frontier topics such as XML databases, NoSQL databases, big data, and analytics. For those reasons, this will be a good book for database professionals who will keep using it for all stages of database studies and works.' J. Leon Zhao, City University of Hong Kong\u003cbr\u003e'This accessible, authoritative book introduces the reader the most important fundamental concepts of data management, while providing a practical view of recent advances. Both are essential for data professionals today.' Foster Provost, New York University, Stern School of Business\u003cbr\u003e'This guide to big and small data management addresses both fundamental principles and practical deployment. It reviews a range of databases and their relevance for analytics. The book is useful to practitioners because it contains many case studies, links to open-source software, and a very useful abstraction of analytics that will help them better choose solutions. It is important to academics because it promotes database principles which are key to successful and sustainable data science.' Sihem Amer-Yahia, Laboratoire d'Informatique de Grenoble and Editor-in-Chief the International Journal on Very Large DataBases\u003cbr\u003e'This book covers everything you will need to teach in a database implementation and design class. With some chapters covering big data, analytic models\/methods, and No-SQL, it can keep our students up-to-date with these new technologies in data management related topics.' Han-fen Hu, University of Nevada, Las Vegas\u003cbr\u003e'As we are entering a new technological era of intelligent machines powered by data-driven algorithms, understanding fundamental concepts of data management and their most current practical applications has become more important than ever. This book is a timely guide for anyone interested in getting up to speed with the state of the art in database systems, big data technologies, and data science. It is full of insightful examples and case studies with direct industrial relevance.' Nesime Tatbul, Intel Labs and Massachusetts Institute of Technology\u003cbr\u003e'It is a pleasure to study this new book on database systems. The book offers a fantastically fresh approach to database teaching. The mix of theoretical and practical contents is almost perfect, the content is up-to-date and covers the recent ones, the examples are nice, and the database testbed provides an excellent way of understanding the concepts. Coupled with the authors 'expertise, this book is an important addition to the database field.' Arnab Bhattacharya, Indian Institute of Technology, Kanpur\u003cbr\u003e'Principles of Database Management is my favorite textbook for teaching a course on database management. Written in a well-illustrated style, this comprehensive book covers essential topics in established data management technologies and recent discoveries in data science. With a nice balance between theory and practice, it is not only an excellent teaching medium for students taking information management and\/or data analytics courses, but also a quick and valuable reference for scientists and engineers working in this area.' Chuan Xiao, Graduate School of Informatics, Nagoya University\u003cbr\u003e'Data science success stories and big data applications are only possible because of advances in database technology. This book provides both a broad and deep introduction to databases. It covers the different types of database systems (from relational to noSQL) and manages to bridge the gap between data modeling and the underlying basic principles. The book is highly recommended for anyone that wants to understand how modern information systems deal with ever-growing volumes of data.' Wil van der Aalst, RWTH Aachen University\u003cbr\u003e'The database field has been evolving for several decades and the need for updated textbooks is continuous. Now, this need is covered by this fresh book by Lemahieu, van den Broucke and Baesens. It spans from traditional topics - such as the relational model and SQL - to more recent topics – such as distributed computing with Hadoop and Spark as well as data analytics. The book can be used as an introductory text and for graduate courses.' Yannis Manolopoulos, Data Science \u0026amp; Engineering Lab, Aristotle University of Thessaloniki\u003cbr\u003e'I like the way the book covers both traditional database topics and newer material such as big data, No-SQL databases, and data quality. The coverage is just right for my course and the level of the material is very appropriate for my students. The book also has clear explanations and good examples.' Barbara Klein, University of Michigan\u003cbr\u003eThis book provides a unique perspective on database management and how to store, manage, and analyze small and big data. The accompanying exercises and solutions, cases, slides, and YouTube lectures turn it into an indispensable resource for anyone teaching an undergraduate or postgraduate course on the topic.' Wolfgang Ketter, Erasmus University Rotterdam\u003cbr\u003e'This is a very modern textbook that fills the needs of current trends without sacrificing the need to cover the required database management systems fundamentals.' George Dimitoglou, Hood College, Maryland\u003cbr\u003e'This book is a much needed foundational piece on data management and data science. The authors successfully integrate the fields of database technology, operations research and big data analytics, which have often been covered independently in the past. A key asset is its didactical approach that builds on a rich set of industry examples and exercises. The book is a must-read for all scholars and practitioners interested in database management, big data analytics and its applications.' Jan Mendling, Institute for Information Business, Vienna\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface; Part I. Databases and Database Design: 1. Fundamental concepts of database management; 2. Architecture and categorization of DBMSs; 3. Conceptual data modeling using the (E)ER model and UML class diagram; 4. Organizational aspects of data management; Part II. Types of Database Systems: 5. Legacy databases; 6. Relational databases: the relational model; 7. Relational databases: structured query language (SQL); 8. Object oriented databases and object persistence; 9. Extended relational databases; 10. XML databases; 11. NoSQL databases; Part III. Physical Data Storage, Transaction Management, and Database Access: 12. Physical file organization and indexing; 13. Physical database organization; 14. Basics of transaction management; 15. Accessing databases and database APIs; 16. Data distribution and distributed transaction management; Part IV. Data Warehousing, Data Governance and (Big) Data Analytics: 17. Data warehousing and business intelligence; 18. Data integration, data quality and data governance; 19. Big data; 20. Analytics; Appendix A. Cases and questions; Appendix B. Using the online environment; Appendix C. Answer key to select review questions; Glossary; Index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48866339553623,"sku":"9781107186125","price":56.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781107186125.jpg?v=1722278206"},{"product_id":"trustworthy-online-controlled-experiments-9781108724265","title":"Trustworthy Online Controlled Experiments","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eGetting numbers is easy; getting trustworthy numbers is hard. From experimentation leaders at Amazon, Google, LinkedIn, and Microsoft, this guide to accelerating innovation using A\/B tests includes practical examples, pitfalls, and advice for students and industry professionals, plus deeper dives into advanced topics for experienced practitioners.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'At the core of the Lean Methodology is the scientific method: Creating hypotheses, running experiments, gathering data, extracting insight and validation or modification of the hypothesis. A\/B testing is the gold standard of creating verifiable and repeatable experiments, and this book is its definitive text.' Steve Blank, Adjunct professor at Stanford University, father of modern entrepreneurship, author of The Startup Owner's Manual and The Four Steps to the Epiphany\u003cbr\u003e'This book is a great resource for executives, leaders, researchers or engineers looking to use online controlled experiments to optimize product features, project efficiency or revenue. I know firsthand the impact that Kohavi's work had on Bing and Microsoft, and I'm excited that these learnings can now reach a wider audience.' Harry Shum, EVP, Microsoft Artificial Intelligence and Research Group\u003cbr\u003e'A great book that is both rigorous and accessible. Readers will learn how to bring trustworthy controlled experiments, which have revolutionized internet product development, to their organizations.' Adam D'Angelo, Co-founder and CEO of Quora and former CTO of Facebook\u003cbr\u003e'This book is a great overview of how several companies use online experimentation and A\/B testing to improve their products. Kohavi, Tang and Xu have a wealth of experience and excellent advice to convey, so the book has lots of practical real world examples and lessons learned over many years of the application of these techniques at scale.' Jeff Dean, Google Senior Fellow and SVP Google Research\u003cbr\u003e'Do you want your organization to make consistently better decisions? This is the new bible of how to get from data to decisions in the digital age. Reading this book is like sitting in meetings inside Amazon, Google, LinkedIn, Microsoft. The authors expose for the first time the way the world's most successful companies make decisions. Beyond the admonitions and anecdotes of normal business books, this book shows what to do and how to do it well. It's the how-to manual for decision-making in the digital world, with dedicated sections for business leaders, engineers, and data analysts.' Scott Cook, Intuit Co-founder \u0026amp; Chairman of the Executive Committee\u003cbr\u003e'Online controlled experiments are powerful tools. Understanding how they work, what their strengths are, and how they can be optimized can illuminate both specialists and a wider audience. This book is the rare combination of technically authoritative, enjoyable to read, and dealing with highly important matters.' John P. A. Ioannidis, Stanford University\u003cbr\u003e'Kohavi, Tang, and Xu are pioneers of online experimentation. The platforms they've built and the experiments they've enabled have transformed some of the largest internet brands. Their research and talks have inspired teams across the industry to adopt experimentation. This book is the authoritative yet practical text that the industry has been waiting for.' Adil Aijaz, Co-founder and CEO, Split Software\u003cbr\u003e'Which online option will be better? We frequently need to make such choices, and frequently err. To determine what will actually work better, we need rigorous controlled experiments, aka A\/B testing. This excellent and lively book by experts from Microsoft, Google, and LinkedIn presents the theory and best practices of A\/B testing. A must read for anyone who does anything online!' Gregory Piatetsky-Shapiro, Ph.D., president of KDnuggets, co-founder of SIGKDD, and LinkedIn Top Voice on Data Science \u0026amp; Analytics\u003cbr\u003e'Ron Kohavi, Diane Tang and Ya Xu are the world's top experts on online experiments. I've been using their work for years and I'm delighted they have now teamed up to write the definitive guide. I recommend this book to all my students and everyone involved in online products and services.' Erik Brynjolfsson, Massachusetts Institute of Technology, co-author of The Second Machine Age\u003cbr\u003e'A modern software-supported business cannot compete successfully without online controlled experimentation. Written by three of the most experienced leaders in the field, this book presents the fundamental principles, illustrates them with compelling examples, and digs deeper to present a wealth of practical advice. It's a 'must read'! Foster Provost, New York University and co-author of the best-selling Data Science for Business\u003cbr\u003e'In the past two decades the technology industry has learned what scientists have known for centuries: that controlled experiments are among the best tools to understand complex phenomena and to solve very challenging problems. The ability to design controlled experiments, run them at scale, and interpret their results is the foundation of how modern high tech businesses operate. Between them the authors have designed and implemented several of the world's most powerful experimentation platforms. This book is a great opportunity to learn from their experiences about how to use these tools and techniques.' Kevin Scott, EVP and CTO of Microsoft\u003cbr\u003e'Online experiments have fueled the success of Amazon, Microsoft, LinkedIn and other leading digital companies. This practical book gives the reader rare access to decades of experimentation experience at these companies and should be on the bookshelf of every data scientist, software engineer and product manager.' Stefan Thomke, William Barclay Harding Professor, Harvard Business School, author of Experimentation Works: The Surprising Power of Business Experiments\u003cbr\u003e'The secret sauce for a successful online business is experimentation. But it is a secret no longer. Here three masters of the art describe the ABCs of A\/B testing so that you too can continuously improve your online services.' Hal Varian, Chief Economist, Google, and author of Intermediate Microeconomics: A Modern Approach\u003cbr\u003e'Experiments are the best tool for online products and services. This book is full of practical knowledge derived from years of successful testing at Microsoft Google and LinkedIn. Insights and best practices are explained with real examples and pitfalls, their markers and solutions identified. I strongly recommend this book!' Preston McAfee, former Chief Economist and VP of Microsoft\u003cbr\u003e'Experimentation is the future of digital strategy and 'Trustworthy Experiments' will be its Bible. Kohavi, Tang and Xu are three of the most noteworthy experts on experimentation working today and their book delivers a truly practical roadmap for digital experimentation that is useful right out of the box. The revealing case studies they conducted over many decades at Microsoft, Amazon, Google and LinkedIn are organized into easy to understand practical lessens with tremendous depth and clarity. It should be required reading for any manager of a digital business.' Sinan Aral, David Austin Professor of Management, Massachusetts Institute of Technology, and author of The Hype Machine\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface – how to read this book; 1. Introduction and motivation; 2. Running and analyzing experiments: an end-to-end example; 3. Twyman's law and experimentation trustworthiness; 4. Experimentation platform and culture; Part II: 5. Speed matters: an end-to-end case study; 6. Organizational metrics; 7. Metrics for experimentation and the Overall Evaluation Criterion (OEC); 8. Institutional memory and aeta-analysis; 9. Ethics in controlled experiments; Part III: 10. Complementary techniques; 11. Observational causal studies; Part IV: 12. Client-side experiments; 13. Instrumentation; 14. Choosing a randomization unit; 15. Ramping experiment exposure: trading off speed, quality, and risk; 16. Scaling experiment analyses; Part V: 17. The statistics behind online controlled experiments; 18. Variance estimation and improved sensitivity: pitfalls and solutions; 19. The A\/A test; 20. Triggering for improved sensitivity; 21. Guardrail metrics; 22. Leakage and interference between variants; 23. Measuring long-term treatment effects.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48866357412183,"sku":"9781108724265","price":29.44,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781108724265.jpg?v=1722278267"},{"product_id":"fraud-analytics-using-descriptive-predictive-and-social-network-techniques-9781119133124","title":"Fraud Analytics Using Descriptive Predictive and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eDetect fraud earlier to mitigate loss and prevent cascading damage   Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques is an authoritative guidebook for setting up a comprehensive fraud detection analytics solution.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eList of Figures xv\u003c\/p\u003e \u003cp\u003eForeword xxiii\u003c\/p\u003e \u003cp\u003ePreface xxv\u003c\/p\u003e \u003cp\u003eAcknowledgments xxix\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Fraud: Detection, Prevention, and Analytics! 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 2\u003c\/p\u003e \u003cp\u003eFraud! 2\u003c\/p\u003e \u003cp\u003eFraud Detection and Prevention 10\u003c\/p\u003e \u003cp\u003eBig Data for Fraud Detection 15\u003c\/p\u003e \u003cp\u003eData-Driven Fraud Detection 17\u003c\/p\u003e \u003cp\u003eFraud-Detection Techniques 19\u003c\/p\u003e \u003cp\u003eFraud Cycle 22\u003c\/p\u003e \u003cp\u003eThe Fraud Analytics Process Model 26\u003c\/p\u003e \u003cp\u003eFraud Data Scientists 30\u003c\/p\u003e \u003cp\u003eA Fraud Data Scientist Should Have Solid Quantitative Skills 30\u003c\/p\u003e \u003cp\u003eA Fraud Data Scientist Should Be a Good Programmer 31\u003c\/p\u003e \u003cp\u003eA Fraud Data Scientist Should Excel in Communication and Visualization Skills 31\u003c\/p\u003e \u003cp\u003eA Fraud Data Scientist Should Have a Solid Business Understanding 32\u003c\/p\u003e \u003cp\u003eA Fraud Data Scientist Should Be Creative 32\u003c\/p\u003e \u003cp\u003eA Scientific Perspective on Fraud 33\u003c\/p\u003e \u003cp\u003eReferences 35\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Data Collection, Sampling, and Preprocessing 37\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 38\u003c\/p\u003e \u003cp\u003eTypes of Data Sources 38\u003c\/p\u003e \u003cp\u003eMerging Data Sources 43\u003c\/p\u003e \u003cp\u003eSampling 45\u003c\/p\u003e \u003cp\u003eTypes of Data Elements 46\u003c\/p\u003e \u003cp\u003eVisual Data Exploration and Exploratory Statistical Analysis 47\u003c\/p\u003e \u003cp\u003eBenford’s Law 48\u003c\/p\u003e \u003cp\u003eDescriptive Statistics 51\u003c\/p\u003e \u003cp\u003eMissing Values 52\u003c\/p\u003e \u003cp\u003eOutlier Detection and Treatment 53\u003c\/p\u003e \u003cp\u003eRed Flags 57\u003c\/p\u003e \u003cp\u003eStandardizing Data 59\u003c\/p\u003e \u003cp\u003eCategorization 60\u003c\/p\u003e \u003cp\u003eWeights of Evidence Coding 63\u003c\/p\u003e \u003cp\u003eVariable Selection 65\u003c\/p\u003e \u003cp\u003ePrincipal Components Analysis 68\u003c\/p\u003e \u003cp\u003eRIDITs 72\u003c\/p\u003e \u003cp\u003ePRIDIT Analysis 73\u003c\/p\u003e \u003cp\u003eSegmentation 74\u003c\/p\u003e \u003cp\u003eReferences 75\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Descriptive Analytics for Fraud Detection 77\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 78\u003c\/p\u003e \u003cp\u003eGraphical Outlier Detection Procedures 79\u003c\/p\u003e \u003cp\u003eStatistical Outlier Detection Procedures 83\u003c\/p\u003e \u003cp\u003eBreak-Point Analysis 84\u003c\/p\u003e \u003cp\u003ePeer-Group Analysis 85\u003c\/p\u003e \u003cp\u003eAssociation Rule Analysis 87\u003c\/p\u003e \u003cp\u003eClustering 89\u003c\/p\u003e \u003cp\u003eIntroduction 89\u003c\/p\u003e \u003cp\u003eDistance Metrics 90\u003c\/p\u003e \u003cp\u003eHierarchical Clustering 94\u003c\/p\u003e \u003cp\u003eExample of Hierarchical Clustering Procedures 97\u003c\/p\u003e \u003cp\u003e\u003ci\u003ek-Means Clustering 104\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eSelf-Organizing Maps 109\u003c\/p\u003e \u003cp\u003eClustering with Constraints 111\u003c\/p\u003e \u003cp\u003eEvaluating and Interpreting Clustering Solutions 114\u003c\/p\u003e \u003cp\u003eOne-Class SVMs 117\u003c\/p\u003e \u003cp\u003eReferences 118\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Predictive Analytics for Fraud Detection 121\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 122\u003c\/p\u003e \u003cp\u003eTarget Definition 123\u003c\/p\u003e \u003cp\u003eLinear Regression 125\u003c\/p\u003e \u003cp\u003eLogistic Regression 127\u003c\/p\u003e \u003cp\u003eBasic Concepts 127\u003c\/p\u003e \u003cp\u003eLogistic Regression Properties 129\u003c\/p\u003e \u003cp\u003eBuilding a Logistic Regression Scorecard 131\u003c\/p\u003e \u003cp\u003eVariable Selection for Linear and Logistic Regression 133\u003c\/p\u003e \u003cp\u003eDecision Trees 136\u003c\/p\u003e \u003cp\u003eBasic Concepts 136\u003c\/p\u003e \u003cp\u003eSplitting Decision 137\u003c\/p\u003e \u003cp\u003eStopping Decision 140\u003c\/p\u003e \u003cp\u003eDecision Tree Properties 141\u003c\/p\u003e \u003cp\u003eRegression Trees 142\u003c\/p\u003e \u003cp\u003eUsing Decision Trees in Fraud Analytics 143\u003c\/p\u003e \u003cp\u003eNeural Networks 144\u003c\/p\u003e \u003cp\u003eBasic Concepts 144\u003c\/p\u003e \u003cp\u003eWeight Learning 147\u003c\/p\u003e \u003cp\u003eOpening the Neural Network Black Box 150\u003c\/p\u003e \u003cp\u003eSupport Vector Machines 155\u003c\/p\u003e \u003cp\u003eLinear Programming 155\u003c\/p\u003e \u003cp\u003eThe Linear Separable Case 156\u003c\/p\u003e \u003cp\u003eThe Linear Nonseparable Case 159\u003c\/p\u003e \u003cp\u003eThe Nonlinear SVM Classifier 160\u003c\/p\u003e \u003cp\u003eSVMs for Regression 161\u003c\/p\u003e \u003cp\u003eOpening the SVM Black Box 163\u003c\/p\u003e \u003cp\u003eEnsemble Methods 164\u003c\/p\u003e \u003cp\u003eBagging 164\u003c\/p\u003e \u003cp\u003eBoosting 165\u003c\/p\u003e \u003cp\u003eRandom Forests 166\u003c\/p\u003e \u003cp\u003eEvaluating Ensemble Methods 167\u003c\/p\u003e \u003cp\u003eMulticlass Classification Techniques 168\u003c\/p\u003e \u003cp\u003eMulticlass Logistic Regression 168\u003c\/p\u003e \u003cp\u003eMulticlass Decision Trees 170\u003c\/p\u003e \u003cp\u003eMulticlass Neural Networks 170\u003c\/p\u003e \u003cp\u003eMulticlass Support Vector Machines 171\u003c\/p\u003e \u003cp\u003eEvaluating Predictive Models 172\u003c\/p\u003e \u003cp\u003eSplitting Up the Data Set 172\u003c\/p\u003e \u003cp\u003ePerformance Measures for Classification Models 176\u003c\/p\u003e \u003cp\u003ePerformance Measures for Regression Models 185\u003c\/p\u003e \u003cp\u003eOther Performance Measures for Predictive Analytical Models 188\u003c\/p\u003e \u003cp\u003eDeveloping Predictive Models for Skewed Data Sets 189\u003c\/p\u003e \u003cp\u003eVarying the Sample Window 190\u003c\/p\u003e \u003cp\u003eUndersampling and Oversampling 190\u003c\/p\u003e \u003cp\u003eSynthetic Minority Oversampling Technique (SMOTE) 192\u003c\/p\u003e \u003cp\u003eLikelihood Approach 194\u003c\/p\u003e \u003cp\u003eAdjusting Posterior Probabilities 197\u003c\/p\u003e \u003cp\u003eCost-sensitive Learning 198\u003c\/p\u003e \u003cp\u003eFraud Performance Benchmarks 200\u003c\/p\u003e \u003cp\u003eReferences 201\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Social Network Analysis for Fraud Detection 207\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eNetworks: Form, Components, Characteristics, and Their Applications 209\u003c\/p\u003e \u003cp\u003eSocial Networks 211\u003c\/p\u003e \u003cp\u003eNetwork Components 214\u003c\/p\u003e \u003cp\u003eNetwork Representation 219\u003c\/p\u003e \u003cp\u003eIs Fraud a Social Phenomenon? An Introduction to Homophily 222\u003c\/p\u003e \u003cp\u003eImpact of the Neighborhood: Metrics 227\u003c\/p\u003e \u003cp\u003eNeighborhood Metrics 228\u003c\/p\u003e \u003cp\u003eCentrality Metrics 238\u003c\/p\u003e \u003cp\u003eCollective Inference Algorithms 246\u003c\/p\u003e \u003cp\u003eFeaturization: Summary Overview 254\u003c\/p\u003e \u003cp\u003eCommunity Mining: Finding Groups of Fraudsters 254\u003c\/p\u003e \u003cp\u003eExtending the Graph: Toward a Bipartite Representation 266\u003c\/p\u003e \u003cp\u003eMultipartite Graphs 269\u003c\/p\u003e \u003cp\u003eCase Study: Gotcha! 270\u003c\/p\u003e \u003cp\u003eReferences 277\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Fraud Analytics: Post-Processing 279\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 280\u003c\/p\u003e \u003cp\u003eThe Analytical Fraud Model Life Cycle 280\u003c\/p\u003e \u003cp\u003eModel Representation 281\u003c\/p\u003e \u003cp\u003eTraffic Light Indicator Approach 282\u003c\/p\u003e \u003cp\u003eDecision Tables 283\u003c\/p\u003e \u003cp\u003eSelecting the Sample to Investigate 286\u003c\/p\u003e \u003cp\u003eFraud Alert and Case Management 290\u003c\/p\u003e \u003cp\u003eVisual Analytics 296\u003c\/p\u003e \u003cp\u003eBacktesting Analytical Fraud Models 302\u003c\/p\u003e \u003cp\u003eIntroduction 302\u003c\/p\u003e \u003cp\u003eBacktesting Data Stability 302\u003c\/p\u003e \u003cp\u003eBacktesting Model Stability 305\u003c\/p\u003e \u003cp\u003eBacktesting Model Calibration 308\u003c\/p\u003e \u003cp\u003eModel Design and Documentation 311\u003c\/p\u003e \u003cp\u003eReferences 312\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Fraud Analytics: A Broader Perspective 313\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 314\u003c\/p\u003e \u003cp\u003eData Quality 314\u003c\/p\u003e \u003cp\u003eData-Quality Issues 314\u003c\/p\u003e \u003cp\u003eData-Quality Programs and Management 315\u003c\/p\u003e \u003cp\u003ePrivacy 317\u003c\/p\u003e \u003cp\u003eThe RACI Matrix 318\u003c\/p\u003e \u003cp\u003eAccessing Internal Data 319\u003c\/p\u003e \u003cp\u003eLabel-Based Access Control (LBAC) 324\u003c\/p\u003e \u003cp\u003eAccessing External Data 325\u003c\/p\u003e \u003cp\u003eCapital Calculation for Fraud Loss 326\u003c\/p\u003e \u003cp\u003eExpected and Unexpected Losses 327\u003c\/p\u003e \u003cp\u003eAggregate Loss Distribution 329\u003c\/p\u003e \u003cp\u003eCapital Calculation for Fraud Loss Using Monte Carlo Simulation 331\u003c\/p\u003e \u003cp\u003eAn Economic Perspective on Fraud Analytics 334\u003c\/p\u003e \u003cp\u003eTotal Cost of Ownership 334\u003c\/p\u003e \u003cp\u003eReturn on Investment 335\u003c\/p\u003e \u003cp\u003eIn Versus Outsourcing 337\u003c\/p\u003e \u003cp\u003eModeling Extensions 338\u003c\/p\u003e \u003cp\u003eForecasting 338\u003c\/p\u003e \u003cp\u003eText Analytics 340\u003c\/p\u003e \u003cp\u003eThe Internet of Things 342\u003c\/p\u003e \u003cp\u003eCorporate Fraud Governance 344\u003c\/p\u003e \u003cp\u003eReferences 346\u003c\/p\u003e \u003cp\u003eAbout the Authors 347\u003c\/p\u003e \u003cp\u003eIndex 349 \u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48866387427671,"sku":"9781119133124","price":31.2,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119133124.jpg?v=1722278409"},{"product_id":"smarter-data-science-9781119693413","title":"Smarter Data Science","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eOrganizations can make data science a repeatable, predictable tool, which business professionals use to get more value from their data\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEnterprise data and AI projects are often scattershot, underbaked, siloed, and not adaptable to predictable business changes. As a result, the vast majority fail. These expensive quagmires can be avoided, and this book explains precisely how.\u003c\/p\u003e \u003cp\u003eData science is emerging as a hands-on tool for not just data scientists, but business professionals as well. Managers, directors, IT leaders, and analysts must expand their use of data science capabilities for the organization to stay competitive.\u003ci\u003e Smarter Data Science\u003c\/i\u003e helps them achieve their enterprise-grade data projects and AI goals. It serves as a guide to building a robust and comprehensive information architecture program that enables sustainable and scalable AI deployments.\u003c\/p\u003e \u003cp\u003eWhen an organization manages its data effectively, its data science program becomes a fully scala\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eForeword for \u003ci\u003eSmarter Data Science \u003c\/i\u003exix\u003c\/p\u003e \u003cp\u003eEpigraph xxi\u003c\/p\u003e \u003cp\u003ePreamble xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Climbing the AI Ladder 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReadying Data for AI 2\u003c\/p\u003e \u003cp\u003eTechnology Focus Areas 3\u003c\/p\u003e \u003cp\u003eTaking the Ladder Rung by Rung 4\u003c\/p\u003e \u003cp\u003eConstantly Adapt to Retain Organizational Relevance 8\u003c\/p\u003e \u003cp\u003eData-Based Reasoning is Part and Parcel in the Modern Business 10\u003c\/p\u003e \u003cp\u003eToward the AI-Centric Organization 14\u003c\/p\u003e \u003cp\u003eSummary 16\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Framing Part I: Considerations for Organizations Using AI 17\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData-Driven Decision-Making 18\u003c\/p\u003e \u003cp\u003eUsing Interrogatives to Gain Insight 19\u003c\/p\u003e \u003cp\u003eThe Trust Matrix 20\u003c\/p\u003e \u003cp\u003eThe Importance of Metrics and Human Insight 22\u003c\/p\u003e \u003cp\u003eDemocratizing Data and Data Science 23\u003c\/p\u003e \u003cp\u003eAye, a Prerequisite: Organizing Data Must Be a Forethought 26\u003c\/p\u003e \u003cp\u003ePreventing Design Pitfalls 27\u003c\/p\u003e \u003cp\u003eFacilitating the Winds of Change: How Organized Data Facilitates Reaction Time 29\u003c\/p\u003e \u003cp\u003e\u003ci\u003eQuae Quaestio \u003c\/i\u003e(Question Everything) 30\u003c\/p\u003e \u003cp\u003eSummary 32\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Framing Part II: Considerations for Working with Data and AI 35\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePersonalizing the Data Experience for Every User 36\u003c\/p\u003e \u003cp\u003eContext Counts: Choosing the Right Way to Display Data 38\u003c\/p\u003e \u003cp\u003eEthnography: Improving Understanding Through Specialized Data 42\u003c\/p\u003e \u003cp\u003eData Governance and Data Quality 43\u003c\/p\u003e \u003cp\u003eThe Value of Decomposing Data 43\u003c\/p\u003e \u003cp\u003eProviding Structure Through Data Governance 43\u003c\/p\u003e \u003cp\u003eCurating Data for Training 45\u003c\/p\u003e \u003cp\u003eAdditional Considerations for Creating Value 45\u003c\/p\u003e \u003cp\u003eOntologies: A Means for Encapsulating Knowledge 46\u003c\/p\u003e \u003cp\u003eFairness, Trust, and Transparency in AI Outcomes 49\u003c\/p\u003e \u003cp\u003eAccessible, Accurate, Curated, and Organized 52\u003c\/p\u003e \u003cp\u003eSummary 54\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 A Look Back on Analytics: More Than One Hammer 57\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBeen Here Before: Reviewing the Enterprise Data Warehouse 57\u003c\/p\u003e \u003cp\u003eDrawbacks of the Traditional Data Warehouse 64\u003c\/p\u003e \u003cp\u003eParadigm Shift 68\u003c\/p\u003e \u003cp\u003eModern Analytical Environments: The Data Lake 69\u003c\/p\u003e \u003cp\u003eBy Contrast 71\u003c\/p\u003e \u003cp\u003eIndigenous Data 72\u003c\/p\u003e \u003cp\u003eAttributes of Difference 73\u003c\/p\u003e \u003cp\u003eElements of the Data Lake 75\u003c\/p\u003e \u003cp\u003eThe New Normal: Big Data is Now Normal Data 77\u003c\/p\u003e \u003cp\u003eLiberation from the Rigidity of a Single Data Model 78\u003c\/p\u003e \u003cp\u003eStreaming Data 78\u003c\/p\u003e \u003cp\u003eSuitable Tools for the Task 78\u003c\/p\u003e \u003cp\u003eEasier Accessibility 79\u003c\/p\u003e \u003cp\u003eReducing Costs 79\u003c\/p\u003e \u003cp\u003eScalability 79\u003c\/p\u003e \u003cp\u003eData Management and Data Governance for AI 80\u003c\/p\u003e \u003cp\u003eSchema-on-Read vs. Schema-on-Write 81\u003c\/p\u003e \u003cp\u003eSummary 84\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 A Look Forward on Analytics: Not Everything Can Be a Nail 87\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA Need for Organization 87\u003c\/p\u003e \u003cp\u003eThe Staging Zone 90\u003c\/p\u003e \u003cp\u003eThe Raw Zone 91\u003c\/p\u003e \u003cp\u003eThe Discovery and Exploration Zone 92\u003c\/p\u003e \u003cp\u003eThe Aligned Zone 93\u003c\/p\u003e \u003cp\u003eThe Harmonized Zone 98\u003c\/p\u003e \u003cp\u003eThe Curated Zone 100\u003c\/p\u003e \u003cp\u003eData Topologies 100\u003c\/p\u003e \u003cp\u003eZone Map 103\u003c\/p\u003e \u003cp\u003eData Pipelines 104\u003c\/p\u003e \u003cp\u003eData Topography 105\u003c\/p\u003e \u003cp\u003eExpanding, Adding, Moving, and Removing Zones 107\u003c\/p\u003e \u003cp\u003eEnabling the Zones 108\u003c\/p\u003e \u003cp\u003eIngestion 108\u003c\/p\u003e \u003cp\u003eData Governance 111\u003c\/p\u003e \u003cp\u003eData Storage and Retention 112\u003c\/p\u003e \u003cp\u003eData Processing 114\u003c\/p\u003e \u003cp\u003eData Access 116\u003c\/p\u003e \u003cp\u003eManagement and Monitoring 117\u003c\/p\u003e \u003cp\u003eMetadata 118\u003c\/p\u003e \u003cp\u003eSummary 119\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Addressing Operational Disciplines on the AI Ladder 121\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA Passage of Time 122\u003c\/p\u003e \u003cp\u003eCreate 128\u003c\/p\u003e \u003cp\u003eStability 128\u003c\/p\u003e \u003cp\u003eBarriers 129\u003c\/p\u003e \u003cp\u003eComplexity 129\u003c\/p\u003e \u003cp\u003eExecute 130\u003c\/p\u003e \u003cp\u003eIngestion 131\u003c\/p\u003e \u003cp\u003eVisibility 132\u003c\/p\u003e \u003cp\u003eCompliance 132\u003c\/p\u003e \u003cp\u003eOperate 133\u003c\/p\u003e \u003cp\u003eQuality 134\u003c\/p\u003e \u003cp\u003eReliance 135\u003c\/p\u003e \u003cp\u003eReusability 135\u003c\/p\u003e \u003cp\u003eThe xOps Trifecta: DevOps\/MLOps, DataOps, and AIOps 136\u003c\/p\u003e \u003cp\u003eDevOps\/MLOps 137\u003c\/p\u003e \u003cp\u003eDataOps 139\u003c\/p\u003e \u003cp\u003eAIOps 142\u003c\/p\u003e \u003cp\u003eSummary 144\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Maximizing the Use of Your Data: Being Value Driven 147\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eToward a Value Chain 148\u003c\/p\u003e \u003cp\u003eChaining Through Correlation 152\u003c\/p\u003e \u003cp\u003eEnabling Action 154\u003c\/p\u003e \u003cp\u003eExpanding the Means to Act 155\u003c\/p\u003e \u003cp\u003eCuration 156\u003c\/p\u003e \u003cp\u003eData Governance 159\u003c\/p\u003e \u003cp\u003eIntegrated Data Management 162\u003c\/p\u003e \u003cp\u003eOnboarding 163\u003c\/p\u003e \u003cp\u003eOrganizing 164\u003c\/p\u003e \u003cp\u003eCataloging 166\u003c\/p\u003e \u003cp\u003eMetadata 167\u003c\/p\u003e \u003cp\u003ePreparing 168\u003c\/p\u003e \u003cp\u003eProvisioning 169\u003c\/p\u003e \u003cp\u003eMulti-Tenancy 170\u003c\/p\u003e \u003cp\u003eSummary 173\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Valuing Data with Statistical Analysis and Enabling Meaningful Access 175\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDeriving Value: Managing Data as an Asset 175\u003c\/p\u003e \u003cp\u003eAn Inexact Science 180\u003c\/p\u003e \u003cp\u003eAccessibility to Data: Not All Users are Equal 183\u003c\/p\u003e \u003cp\u003eProviding Self-Service to Data 184\u003c\/p\u003e \u003cp\u003eAccess: The Importance of Adding Controls 186\u003c\/p\u003e \u003cp\u003eRanking Datasets Using a Bottom-Up Approach for Data Governance 187\u003c\/p\u003e \u003cp\u003eHow Various Industries Use Data and AI 188\u003c\/p\u003e \u003cp\u003eBenefi ting from Statistics 189\u003c\/p\u003e \u003cp\u003eSummary 198\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Constructing for the Long-Term 199\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Need to Change Habits: Avoiding Hard-Coding 200\u003c\/p\u003e \u003cp\u003eOverloading 201\u003c\/p\u003e \u003cp\u003eLocked In 202\u003c\/p\u003e \u003cp\u003eOwnership and Decomposition 204\u003c\/p\u003e \u003cp\u003eDesign to Avoid Change 204\u003c\/p\u003e \u003cp\u003eExtending the Value of Data Through AI 206\u003c\/p\u003e \u003cp\u003ePolyglot Persistence 208\u003c\/p\u003e \u003cp\u003eBenefi ting from Data Literacy 213\u003c\/p\u003e \u003cp\u003eUnderstanding a Topic 215\u003c\/p\u003e \u003cp\u003eSkillsets 216\u003c\/p\u003e \u003cp\u003eIt’s All Metadata 218\u003c\/p\u003e \u003cp\u003eThe Right Data, in the Right Context, with the Right Interface 219\u003c\/p\u003e \u003cp\u003eSummary 221\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 A Journey’s End: An IA for AI 223\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDevelopment Efforts for AI 224\u003c\/p\u003e \u003cp\u003eEssential Elements: Cloud-Based Computing, Data, and Analytics 228\u003c\/p\u003e \u003cp\u003eIntersections: Compute Capacity and Storage Capacity 234\u003c\/p\u003e \u003cp\u003eAnalytic Intensity 237\u003c\/p\u003e \u003cp\u003eInteroperability Across the Elements 238\u003c\/p\u003e \u003cp\u003eData Pipeline Flight Paths: Preflight, Inflight, Postflight 242\u003c\/p\u003e \u003cp\u003eData Management for the Data Puddle, Data Pond, and Data Lake 243\u003c\/p\u003e \u003cp\u003eDriving Action: Context, Content, and Decision-Makers 245\u003c\/p\u003e \u003cp\u003eKeep It Simple 248\u003c\/p\u003e \u003cp\u003eThe Silo is Dead; Long Live the Silo 250\u003c\/p\u003e \u003cp\u003eTaxonomy: Organizing Data Zones 252\u003c\/p\u003e \u003cp\u003eCapabilities for an Open Platform 256\u003c\/p\u003e \u003cp\u003eSummary 260\u003c\/p\u003e \u003cp\u003eAppendix Glossary of Terms 263\u003c\/p\u003e \u003cp\u003eIndex 269\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48866411348311,"sku":"9781119693413","price":30.39,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119693413.jpg?v=1722278512"},{"product_id":"responsible-data-science-9781119741756","title":"Responsible Data Science","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eExplore the most serious prevalent ethical issues in data science with this insightful new resource The increasing popularity of data science has resulted in numerous well-publicized cases of bias, injustice, and discrimination. The widespread deployment of Black box algorithms that are difficult or impossible to understand and explain, even for their developers, is a primary source of these unanticipated harms, making modern techniques and methods for manipulating large data sets seem sinister, even dangerous. When put in the hands of authoritarian governments, these algorithms have enabled suppression of political dissent and persecution of minorities. To prevent these harms, data scientists everywhere must come to understand how the algorithms that they build and deploy may harm certain groups or be unfair.    Responsible Data Science delivers a comprehensive, practical treatment of how to implement data science solutions in an even-handed and ethical manner that minimizes the risk \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Motivation for Ethical Data Science and Background Knowledge 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Responsible Data Science 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Optum Disaster 4\u003c\/p\u003e \u003cp\u003eJekyll and Hyde 5\u003c\/p\u003e \u003cp\u003eEugenics 7\u003c\/p\u003e \u003cp\u003eGalton, Pearson, and Fisher 7\u003c\/p\u003e \u003cp\u003eTies between Eugenics and Statistics 7\u003c\/p\u003e \u003cp\u003eEthical Problems in Data Science Today 9\u003c\/p\u003e \u003cp\u003ePredictive Models 10\u003c\/p\u003e \u003cp\u003eFrom Explaining to Predicting 10\u003c\/p\u003e \u003cp\u003ePredictive Modeling 11\u003c\/p\u003e \u003cp\u003eSetting the Stage for Ethical Issues to Arise 12\u003c\/p\u003e \u003cp\u003eClassic Statistical Models 12\u003c\/p\u003e \u003cp\u003eBlack-Box Methods 14\u003c\/p\u003e \u003cp\u003eImportant Concepts in Predictive Modeling 19\u003c\/p\u003e \u003cp\u003eFeature Selection 19\u003c\/p\u003e \u003cp\u003eModel-Centric vs. Data-Centric Models 20\u003c\/p\u003e \u003cp\u003eHoldout Sample and Cross-Validation 20\u003c\/p\u003e \u003cp\u003eOverfitting 21\u003c\/p\u003e \u003cp\u003eUnsupervised Learning 22\u003c\/p\u003e \u003cp\u003eThe Ethical Challenge of Black Boxes 23\u003c\/p\u003e \u003cp\u003eTwo Opposing Forces 24\u003c\/p\u003e \u003cp\u003ePressure for More Powerful AI 24\u003c\/p\u003e \u003cp\u003ePublic Resistance and Anxiety 24\u003c\/p\u003e \u003cp\u003eSummary 25\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Background: Modeling and the Black-Box Algorithm 27\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAssessing Model Performance 27\u003c\/p\u003e \u003cp\u003ePredicting Class Membership 28\u003c\/p\u003e \u003cp\u003eThe Rare Class Problem 28\u003c\/p\u003e \u003cp\u003eLift and Gains 28\u003c\/p\u003e \u003cp\u003eArea Under the Curve 29\u003c\/p\u003e \u003cp\u003eAUC vs. Lift (Gains) 31\u003c\/p\u003e \u003cp\u003ePredicting Numeric Values 32\u003c\/p\u003e \u003cp\u003eGoodness-of-Fit 32\u003c\/p\u003e \u003cp\u003eHoldout Sets and Cross-Validation 33\u003c\/p\u003e \u003cp\u003eOptimization and Loss Functions 34\u003c\/p\u003e \u003cp\u003eIntrinsically Interpretable Models vs. Black-Box Models 35\u003c\/p\u003e \u003cp\u003eEthical Challenges with Interpretable Models 38\u003c\/p\u003e \u003cp\u003eBlack-Box Models 39\u003c\/p\u003e \u003cp\u003eEnsembles 39\u003c\/p\u003e \u003cp\u003eNearest Neighbors 41\u003c\/p\u003e \u003cp\u003eClustering 41\u003c\/p\u003e \u003cp\u003eAssociation Rules 42\u003c\/p\u003e \u003cp\u003eCollaborative Filters 42\u003c\/p\u003e \u003cp\u003eArtificial Neural Nets and Deep Neural Nets 43\u003c\/p\u003e \u003cp\u003eProblems with Black-Box Predictive Models 45\u003c\/p\u003e \u003cp\u003eProblems with Unsupervised Algorithms 47\u003c\/p\u003e \u003cp\u003eSummary 48\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 The Ways AI Goes Wrong, and the Legal Implications 49\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAI and Intentional Consequences by Design 50\u003c\/p\u003e \u003cp\u003eDeepfakes 50\u003c\/p\u003e \u003cp\u003eSupporting State Surveillance and Suppression 51\u003c\/p\u003e \u003cp\u003eBehavioral Manipulation 52\u003c\/p\u003e \u003cp\u003eAutomated Testing to Fine-Tune Targeting 53\u003c\/p\u003e \u003cp\u003eAI and Unintended Consequences 55\u003c\/p\u003e \u003cp\u003eHealthcare 56\u003c\/p\u003e \u003cp\u003eFinance 57\u003c\/p\u003e \u003cp\u003eLaw Enforcement 58\u003c\/p\u003e \u003cp\u003eTechnology 60\u003c\/p\u003e \u003cp\u003eThe Legal and Regulatory Landscape around AI 61\u003c\/p\u003e \u003cp\u003eIgnorance Is No Defense: AI in the Context of Existing Law and Policy 63\u003c\/p\u003e \u003cp\u003eA Finger in the Dam: Data Rights, Data Privacy, and Consumer Protection Regulations 64\u003c\/p\u003e \u003cp\u003eTrends in Emerging Law and Policy Related to AI 66\u003c\/p\u003e \u003cp\u003eSummary 69\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II The Ethical Data Science Process 71\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 The Responsible Data Science Framework 73\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhy We Keep Building Harmful AI 74\u003c\/p\u003e \u003cp\u003eMisguided Need for Cutting-Edge Models 74\u003c\/p\u003e \u003cp\u003eExcessive Focus on Predictive Performance 74\u003c\/p\u003e \u003cp\u003eEase of Access and the Curse of Simplicity 76\u003c\/p\u003e \u003cp\u003eThe Common Cause 76\u003c\/p\u003e \u003cp\u003eThe Face Thieves 78\u003c\/p\u003e \u003cp\u003eAn Anatomy of Modeling Harms 79\u003c\/p\u003e \u003cp\u003eThe World: Context Matters for Modeling 80\u003c\/p\u003e \u003cp\u003eThe Data: Representation Is Everything 83\u003c\/p\u003e \u003cp\u003eThe Model: Garbage In, Danger Out 85\u003c\/p\u003e \u003cp\u003eModel Interpretability: Human Understanding for Superhuman Models 86\u003c\/p\u003e \u003cp\u003eEfforts Toward a More Responsible Data Science 89\u003c\/p\u003e \u003cp\u003ePrinciples Are the Focus 90\u003c\/p\u003e \u003cp\u003eNonmaleficence 90\u003c\/p\u003e \u003cp\u003eFairness 90\u003c\/p\u003e \u003cp\u003eTransparency 91\u003c\/p\u003e \u003cp\u003eAccountability 91\u003c\/p\u003e \u003cp\u003ePrivacy 92\u003c\/p\u003e \u003cp\u003eBridging the Gap Between Principles and Practice with the Responsible Data Science (RDS) Framework 92\u003c\/p\u003e \u003cp\u003eJustification 94\u003c\/p\u003e \u003cp\u003eCompilation 94\u003c\/p\u003e \u003cp\u003ePreparation 95\u003c\/p\u003e \u003cp\u003eModeling 96\u003c\/p\u003e \u003cp\u003eAuditing 96\u003c\/p\u003e \u003cp\u003eSummary 97\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Model Interpretability: The What and the Why 99\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Sexist Résumé Screener 99\u003c\/p\u003e \u003cp\u003eThe Necessity of Model Interpretability 101\u003c\/p\u003e \u003cp\u003eConnections Between Predictive Performance and Interpretability 103\u003c\/p\u003e \u003cp\u003eUniting (High) Model Performance and Model Interpretability 105\u003c\/p\u003e \u003cp\u003eCategories of Interpretability Methods 107\u003c\/p\u003e \u003cp\u003eGlobal Methods 107\u003c\/p\u003e \u003cp\u003eLocal Methods 113\u003c\/p\u003e \u003cp\u003eReal-World Successes of Interpretability Methods 113\u003c\/p\u003e \u003cp\u003eFacilitating Debugging and Audit 114\u003c\/p\u003e \u003cp\u003eLeveraging the Improved Performance of Black-Box Models 116\u003c\/p\u003e \u003cp\u003eAcquiring New Knowledge 116\u003c\/p\u003e \u003cp\u003eAddressing Critiques of Interpretability Methods 117\u003c\/p\u003e \u003cp\u003eExplanations Generated by Interpretability Methods Are Not Robust 118\u003c\/p\u003e \u003cp\u003eExplanations Generated by Interpretability Methods Are Low Fidelity 120\u003c\/p\u003e \u003cp\u003eThe Forking Paths of Model Interpretability 121\u003c\/p\u003e \u003cp\u003eThe Four-Measure Baseline 122\u003c\/p\u003e \u003cp\u003eBuilding Our Own Credit Scoring Model 124\u003c\/p\u003e \u003cp\u003eUsing Train-Test Splits 125\u003c\/p\u003e \u003cp\u003eFeature Selection and Feature Engineering 125\u003c\/p\u003e \u003cp\u003eBaseline Models 127\u003c\/p\u003e \u003cp\u003eThe Importance of Making Your Code Work for Everyone 129\u003c\/p\u003e \u003cp\u003eExecution Variability 129\u003c\/p\u003e \u003cp\u003eAddressing Execution Variability with Functionalized Code 130\u003c\/p\u003e \u003cp\u003eStochastic Variability 130\u003c\/p\u003e \u003cp\u003eAddressing Stochastic Variability via Resampling 130\u003c\/p\u003e \u003cp\u003eSummary 133\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III EDS in Practice 135\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Beginning a Responsible Data Science Project 137\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHow the Responsible Data Science Framework Addresses the Common Cause 138\u003c\/p\u003e \u003cp\u003eDatasets Used 140\u003c\/p\u003e \u003cp\u003eRegression Datasets—Communities and Crime 140\u003c\/p\u003e \u003cp\u003eClassification Datasets—COMPAS 140\u003c\/p\u003e \u003cp\u003eCommon Elements Across Our Analyses 141\u003c\/p\u003e \u003cp\u003eProject Structure and Documentation 141\u003c\/p\u003e \u003cp\u003eProject Structure for the Responsible Data\u003c\/p\u003e \u003cp\u003eScience Framework: Everything in Its Place 142\u003c\/p\u003e \u003cp\u003eDocumentation: The Responsible Thing to Do 145\u003c\/p\u003e \u003cp\u003eBeginning a Responsible Data Science Project 151\u003c\/p\u003e \u003cp\u003eCommunities and Crime (Regression) 151\u003c\/p\u003e \u003cp\u003eJustification 151\u003c\/p\u003e \u003cp\u003eCompilation 154\u003c\/p\u003e \u003cp\u003eIdentifying Protected Classes 157\u003c\/p\u003e \u003cp\u003ePreparation—Data Splitting and Feature Engineering 159\u003c\/p\u003e \u003cp\u003eDatasheets 161\u003c\/p\u003e \u003cp\u003eCOMPAS (Classification) 164\u003c\/p\u003e \u003cp\u003eJustification 164\u003c\/p\u003e \u003cp\u003eCompilation 166\u003c\/p\u003e \u003cp\u003eIdentifying Protected Classes 168\u003c\/p\u003e \u003cp\u003ePreparation 169\u003c\/p\u003e \u003cp\u003eSummary 172\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Auditing a Responsible Data Science Project 173\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFairness and Data Science in Practice 175\u003c\/p\u003e \u003cp\u003eThe Many Different Conceptions of Fairness 175\u003c\/p\u003e \u003cp\u003eDifferent Forms of Fairness Are Trade-Offs with Each Other 177\u003c\/p\u003e \u003cp\u003eQuantifying Predictive Fairness Within a Data Science Project 179\u003c\/p\u003e \u003cp\u003eMitigating Bias to Improve Fairness 185\u003c\/p\u003e \u003cp\u003ePreprocessing 185\u003c\/p\u003e \u003cp\u003eIn-processing 186\u003c\/p\u003e \u003cp\u003ePostprocessing 186\u003c\/p\u003e \u003cp\u003eClassification Example: COMPAS 187\u003c\/p\u003e \u003cp\u003ePrework: Code Practices, Modeling, and Auditing 187\u003c\/p\u003e \u003cp\u003eJustification, Compilation, and Preparation Review 189\u003c\/p\u003e \u003cp\u003eModeling 191\u003c\/p\u003e \u003cp\u003eAuditing 200\u003c\/p\u003e \u003cp\u003ePer-Group Metrics: Overall 200\u003c\/p\u003e \u003cp\u003ePer-Group Metrics: Error 202\u003c\/p\u003e \u003cp\u003eFairness Metrics 204\u003c\/p\u003e \u003cp\u003eInterpreting Our Models: Why Are They Unfair? 207\u003c\/p\u003e \u003cp\u003eAnalysis for Different Groups 209\u003c\/p\u003e \u003cp\u003eBias Mitigation 214\u003c\/p\u003e \u003cp\u003ePreprocessing: Oversampling 214\u003c\/p\u003e \u003cp\u003ePostprocessing: Optimizing Thresholds\u003c\/p\u003e \u003cp\u003eAutomatically 218\u003c\/p\u003e \u003cp\u003ePostprocessing: Optimizing Thresholds Manually 219\u003c\/p\u003e \u003cp\u003eSummary 223\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Auditing for Neural Networks 225\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhy Neural Networks Merit Their Own Chapter 227\u003c\/p\u003e \u003cp\u003eNeural Networks Vary Greatly in Structure 227\u003c\/p\u003e \u003cp\u003eNeural Networks Treat Features Differently 229\u003c\/p\u003e \u003cp\u003eNeural Networks Repeat Themselves 231\u003c\/p\u003e \u003cp\u003eA More Impenetrable Black Box 232\u003c\/p\u003e \u003cp\u003eBaseline Methods 233\u003c\/p\u003e \u003cp\u003eRepresentation Methods 233\u003c\/p\u003e \u003cp\u003eDistillation Methods 234\u003c\/p\u003e \u003cp\u003eIntrinsic Methods 235\u003c\/p\u003e \u003cp\u003eBeginning a Responsible Neural Network Project 236\u003c\/p\u003e \u003cp\u003eJustification 236\u003c\/p\u003e \u003cp\u003eMoving Forward 239\u003c\/p\u003e \u003cp\u003eCompilation 239\u003c\/p\u003e \u003cp\u003eTracking Experiments 241\u003c\/p\u003e \u003cp\u003ePreparation 244\u003c\/p\u003e \u003cp\u003eModeling 245\u003c\/p\u003e \u003cp\u003eAuditing 247\u003c\/p\u003e \u003cp\u003ePer-Group Metrics: Overall 247\u003c\/p\u003e \u003cp\u003ePer-Group Metrics: Unusual Definitions of “False Positive” 248\u003c\/p\u003e \u003cp\u003eFairness Metrics 249\u003c\/p\u003e \u003cp\u003eInterpreting Our Models: Why Are They Unfair? 252\u003c\/p\u003e \u003cp\u003eBias Mitigation 253\u003c\/p\u003e \u003cp\u003eWrap-Up 255\u003c\/p\u003e \u003cp\u003eAuditing Neural Networks for Natural Language Processing 258\u003c\/p\u003e \u003cp\u003eIdentifying and Addressing Sources of Bias in NLP 258\u003c\/p\u003e \u003cp\u003eThe Real World 259\u003c\/p\u003e \u003cp\u003eData 260\u003c\/p\u003e \u003cp\u003eModels 261\u003c\/p\u003e \u003cp\u003eModel Interpretability 262\u003c\/p\u003e \u003cp\u003eSummary 262\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Conclusion 265\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHow Can We Do Better? 267\u003c\/p\u003e \u003cp\u003eThe Responsible Data Science Framework 267\u003c\/p\u003e \u003cp\u003eDoing Better As Managers 269\u003c\/p\u003e \u003cp\u003eDoing Better As Practitioners 270\u003c\/p\u003e \u003cp\u003eA Better Future If We Can Keep It 271\u003c\/p\u003e \u003cp\u003eIndex 273\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48866413936983,"sku":"9781119741756","price":24.79,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119741756.jpg?v=1722278526"},{"product_id":"how-colleges-use-data-9781421445199","title":"How Colleges Use Data","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eWhat does a culture of evidence really look like in higher education?The use of big data and the rapid acceleration of storage and analytics tools have led to a revolution of data use in higher education. Institutions have moved from relying largely on historical trends and descriptive data to the more widespread adoption of predictive and prescriptive analytics. Despite this rapid evolution of data technology and analytics tools, universities and colleges still face a number of obstacles in their data use. In How Colleges Use Data, Jonathan S. Gagliardi presents college and university leaders with an important resource to help cultivate, implement, and sustain a culture of evidence through the ethical and responsible use and adoption of data and analytics. Gagliardi provides a broad context for data use among colleges, including key concepts and use cases related to data and analytics. He also addresses the different dimensions of data use and highlights the promise and perils of the \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface\u003cbr\u003eAcknowledgments\u003cbr\u003eChapter 1. The Evidence Imperative\u003cbr\u003eChapter 2. Demystifying Data and Analytics\u003cbr\u003eChapter 3. Defining an Institutional Aspiration Using Data\u003cbr\u003eChapter 4. Equity and Student Success\u003cbr\u003eChapter 5. Strategic Finance and Resource Optimization\u003cbr\u003eChapter 6. Academic Quality and Renewal\u003cbr\u003eChapter 7. Creating a Data Governance System\u003cbr\u003eChapter 8. The Promise and Peril of Data and Analytics\u003cbr\u003eChapter 9. Implementation and Planning\u003cbr\u003eChapter 10. Looking Ahead\u003cbr\u003eNotes\u003cbr\u003eIndex\u003c\/p\u003e","brand":"Johns Hopkins University Press","offers":[{"title":"Default Title","offer_id":48866941403479,"sku":"9781421445199","price":21.6,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781421445199.jpg?v=1722281014"},{"product_id":"beautiful-visualization-looking-at-data-through-the-eyes-of-experts-9781449379865","title":"Beautiful Visualization  Looking At Data Through","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eWith contributions from more than two dozen experts, this book demonstrates why visualizations are beautiful not only for their aesthetic design, but also for elegant layers of detail that efficiently generate insight and new understanding.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48867118022999,"sku":"9781449379865","price":35.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781449379865.jpg?v=1722281775"},{"product_id":"mongodb-the-definitive-guide-3e-9781491954461","title":"MongoDB The Definitive Guide 3e","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eManage your data with a system designed to support modern application development. Updated for MongoDB 4.2, the third edition of this authoritative and accessible guide shows you the advantages of using document-oriented databases.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48867305914711,"sku":"9781491954461","price":39.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781491954461.jpg?v=1722282703"},{"product_id":"data-science-from-scratch-9781492041139","title":"Data Science from Scratch","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eWith this updated second edition, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48867307356503,"sku":"9781492041139","price":39.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781492041139.jpg?v=1722282711"},{"product_id":"semantic-modeling-for-data-9781492054276","title":"Semantic Modeling for Data","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eIn this practical and comprehensive field guide, author Panos Alexopoulos takes you on an eye-opening journey through semantic data modeling as applied in the real world. You’ll learn how to master this craft to increase the usability and value of your data and applications.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48867308306775,"sku":"9781492054276","price":53.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781492054276.jpg?v=1722282714"},{"product_id":"learning-sql-9781492057611","title":"Learning SQL","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eAs data floods into your company, you need to put it to work right away-and SQL is the best tool for the job. With the latest edition of this introductory guide, author Alan Beaulieu helps developers get up to speed with SQL fundamentals for writing database applications, performing administrative tasks, and generating reports.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48867308667223,"sku":"9781492057611","price":39.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781492057611.jpg?v=1722282718"},{"product_id":"tabular-modeling-in-microsoft-sql-server-analysis-services-9781509302772","title":"Tabular Modeling in Microsoft SQL Server Analysis","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eWith SQL Server Analysis Services 2016, Microsoft has dramatically upgraded its Tabular approach to business intelligence data modeling, making Tabular the easiest and best solution for most new projects. In this book, two world-renowned experts in Microsoft data modeling and analysis cover all you need to know to create complete BI solutions with these powerful new tools.\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eMarco Russo and Alberto Ferrari walk you step-by-step through creating powerful data models, and then illuminate advanced features such as optimization, deployment, and scalability.\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003eTabular Modeling in Microsoft SQL Server Analysis Services \u003c\/b\u003ewill be indispensable for everyone moving to Analysis Services Tabular, regardless of their previous experience with tabular-style models or with Microsoft's older Analysis Services offerings. It will also be an essential follow-up for every reader of the authors' highly-praised \u003ci\u003eMicrosoft SQL Server 2012 Analysis Services: The BISM Tabular Model\u003c\/i\u003e.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cul\u003e\n\u003cli\u003e    CHAPTER 1 Introducing the tabular model   \u003c\/li\u003e\n\u003cli\u003e    CHAPTER 2 Getting started with the tabular model   \u003c\/li\u003e\n\u003cli\u003e    CHAPTER 3 Loading data inside Tabular   \u003c\/li\u003e\n\u003cli\u003e    CHAPTER 4 Introducing calculations in DAX   \u003c\/li\u003e\n\u003cli\u003e    CHAPTER 5 Building hierarchies   \u003c\/li\u003e\n\u003cli\u003e    CHAPTER 6 Data modeling in Tabular   \u003c\/li\u003e\n\u003cli\u003e    CHAPTER 7 Tabular Model Scripting Language (TMSL)   \u003c\/li\u003e\n\u003cli\u003e    CHAPTER 8 The tabular presentation layer   \u003c\/li\u003e\n\u003cli\u003e    CHAPTER 9 Using DirectQuery   \u003c\/li\u003e\n\u003cli\u003e    CHAPTER 10 Security   \u003c\/li\u003e\n\u003cli\u003e    CHAPTER 11 Processing and partitioning tabular models   \u003c\/li\u003e\n\u003cli\u003e    CHAPTER 12 Inside VertiPaq   \u003c\/li\u003e\n\u003cli\u003e    CHAPTER 13 Interfacing with Tabular   \u003c\/li\u003e\n\u003cli\u003e    CHAPTER 14 Monitoring and tuning a Tabular service   \u003c\/li\u003e\n\u003cli\u003e    CHAPTER 15 Optimizing tabular models   \u003c\/li\u003e\n\u003cli\u003e    CHAPTER 16 Choosing hardware and virtualization    \u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Microsoft Press,U.S.","offers":[{"title":"Default Title","offer_id":48867386294615,"sku":"9781509302772","price":33.37,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781509302772.jpg?v=1722283063"},{"product_id":"time-series-forecasting-in-python-9781617299889","title":"Time Series Forecasting in Python","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eBuild predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting.\u003c\/b\u003e   \u003cbr\u003e   \u003cbr\u003e   In    \u003ci\u003eT\u003cb\u003eime Series Forecasting in Python\u003c\/b\u003e\u003c\/i\u003e    you will learn how to:   \u003cbr\u003e   \u003cbr\u003e   \u003cul\u003e\n\u003cli\u003eRecognize a time series forecasting problem and build a performant predictive model\u003c\/li\u003e\n\u003cli\u003eCreate univariate forecasting models that account for seasonal effects and external variables\u003c\/li\u003e\n\u003cli\u003eBuild multivariate forecasting models to predict many time series at once\u003c\/li\u003e\n\u003cli\u003eLeverage large datasets by using deep learning for forecasting time series\u003c\/li\u003e\n\u003cli\u003eAutomate the forecasting process\u003c\/li\u003e\n\u003c\/ul\u003e   \u003cbr\u003e   \u003ci\u003e\u003cb\u003eDESCRIPTION \u003c\/b\u003e\u003c\/i\u003e       \u003ci\u003e\u003cb\u003eTime Series Forecasting in Python\u003c\/b\u003e teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You'll explore interesting real-world datasets like Google's daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow.Time Series Forecasting in Python teaches you to apply time series forecasting and get immediate, meaningful predictions. You'll learn both traditional statistical and new deep learning models for time series forecasting, all fully illustrated with Python source code.\u003c\/i\u003e       \u003ci\u003e\u003cbr\u003e\u003c\/i\u003e       \u003ci\u003e\u003cb\u003eTime Series Forecasting in Python\u003c\/b\u003e\u003c\/i\u003e teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You'll explore interesting real-world datasets like Google's daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow.      about the technology  Time series forecasting reveals hidden trends and makes predictions about the future from your data. This powerful technique has proven incredibly valuable across multiple fields—from tracking business metrics, to healthcare and the sciences. Modern Python libraries and powerful deep learning tools have opened up new methods and utilities for making practical time series forecasts.    about the book    \u003ci\u003e\u003cb\u003eTime Series Forecasting in Python\u003c\/b\u003e\u003c\/i\u003e teaches you to apply time series forecasting and get immediate, meaningful predictions. You'll learn both traditional statistical and new deep learning models for time series forecasting, all fully illustrated with Python source code. Test your skills with hands-on projects for forecasting air travel, volume of drug prescriptions, and the earnings of Johnson \u0026amp; Johnson. By the time you're done, you'll be ready to build accurate and insightful forecasting models with tools from the Python ecosystem.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003etable of contents   detailed TOC      PART 1: TIME WAITS FOR NO ONE    READ IN LIVEBOOK  1UNDERSTANDING TIME SERIES FORECASTING      READ IN LIVEBOOK  2A NAÏVE PREDICTION OF THE FUTURE      READ IN LIVEBOOK  3GOING ON A RANDOM WALK    PART 2: FORECASTING WITH STATISTICAL MODELS    READ IN LIVEBOOK  4MODELING A MOVING AVERAGE PROCESS      READ IN LIVEBOOK  5MODELING AN AUTOREGRESSIVE PROCESS      READ IN LIVEBOOK  6MODELING COMPLEX TIME SERIES      READ IN LIVEBOOK  7FORECASTING NON-STATIONARY TIME SERIES      READ IN LIVEBOOK  8ACCOUNTING FOR SEASONALITY      READ IN LIVEBOOK  9ADDING EXTERNAL VARIABLES TO OUR MODEL      READ IN LIVEBOOK  10FORECASTING MULTIPLE TIME SERIES      READ IN LIVEBOOK  11CAPSTONE: FORECASTING THE NUMBER OF ANTIDIABETIC DRUG PRESCRIPTIONS IN AUSTRALIA    PART 3: LARGE-SCALE FORECASTING WITH DEEP LEARNING    READ IN LIVEBOOK  12INTRODUCING DEEP LEARNING FOR TIME SERIES FORECASTING      READ IN LIVEBOOK  13DATA WINDOWING AND CREATING BASELINES FOR DEEP LEARNING      READ IN LIVEBOOK  14BABY STEPS WITH DEEP LEARNING      READ IN LIVEBOOK  15REMEMBERING THE PAST WITH LSTM      READ IN LIVEBOOK  16FILTERING OUR TIME SERIES WITH CNN      READ IN LIVEBOOK  17USING PREDICTIONS TO MAKE MORE PREDICTIONS      READ IN LIVEBOOK  18CAPSTONE: FORECASTING THE ELECTRIC POWER CONSUMPTION OF A HOUSEHOLD    PART 4: AUTOMATING FORECASTING AT SCALE    READ IN LIVEBOOK  19AUTOMATING TIME SERIES FORECASTING WITH PROPHET      READ IN LIVEBOOK  20CAPSTONE: FORECASTING THE MONTHLY AVERAGE RETAIL PRICE OF STEAK IN CANADA      21 GOING ABOVE AND BEYOND    APPENDIX    APPENDIX A: INSTALLATION INSTRUCTIONS","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":48867785310551,"sku":"9781617299889","price":43.69,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781617299889.jpg?v=1722284952"},{"product_id":"fusion-strategy-9781647826253","title":"Fusion Strategy","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eTwo world-renowned experts on innovation and digital strategy explore how real-time data and AI will radically transform physical products—and the companies that make them.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eTech giants like Facebook, Amazon, and Google can collect real-time data from billions of users. For companies that design and manufacture physical products, that type of fluid, data-rich information used to be a pipe dream. Now, with the rise of cheap and powerful sensors, supercomputing, and artificial intelligence, things are changing—fast.\u003c\/p\u003e\u003cp\u003eIn \u003ci\u003eFusion Strategy\u003c\/i\u003e, world-renowned innovation guru Vijay Govindarajan and digital strategy expert Venkat Venkatraman offer a first-of-its-kind playbook that will help industrial companies combine what they do best—create physical products—with what digitals do best—use algorithms and AI to parse expansive, interconnected datasets—to make strategic connections that would otherwise be impossible.\u003c\/p\u003e\u003cp\u003eThe laws of\u003c\/p\u003e","brand":"Harvard Business Review Press","offers":[{"title":"Default Title","offer_id":48867996041559,"sku":"9781647826253","price":23.75,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781647826253.jpg?v=1722285961"},{"product_id":"data-visualization-9780357631348","title":"Data Visualization","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eDATA VISUALIZATION: Exploring and Explaining with Data is designed to introduce best practices in data visualization to undergraduate and graduate students. This is one of the first books on data visualization designed for college courses. The book contains material on effective design, choice of chart type, effective use of color, how to both explore data visually, and how to explain concepts and results visually in a compelling way with data. The book explains both the \"why\" of data visualization and the \"how.\" That is, the book provides lucid explanations of the guiding principles of data visualization through the use of interesting examples.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction. 2. Selecting a Chart Type. 3. Data Visualization and Design. 4. Purposeful Use of Color. 5. Visualizing Variability. 6. Exploring Data Visually. 7. Explaining Visually to Influence with Data. 8. Data Dashboards. 9. Telling the Truth with Data Visualization.","brand":"Cengage Learning, Inc","offers":[{"title":"Default Title","offer_id":48884050297175,"sku":"9780357631348","price":58.89,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780357631348.jpg?v=1722530208"},{"product_id":"beginning-apache-spark-2-9781484235782","title":"Beginning Apache Spark 2","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cdiv\u003eDevelop applications for the big data landscape with Spark and Hadoop. This book also explains the role of Spark in developing scalable machine learning and analytics applications with Cloud technologies. \u003ci\u003eBeginning Apache Spark 2\u003c\/i\u003e gives you an introduction to Apache Spark and shows you how to work with it.\u003c\/div\u003e\u003cdiv\u003e\u003cbr\u003e\u003c\/div\u003e\u003cdiv\u003eAlong the way, you'll discover resilient distributed datasets (RDDs); use Spark SQL for structured data; and learn stream processing and build real-time applications with Spark Structured Streaming. Furthermore, you'll learn the fundamentals of Spark ML for machine learning and much more. \u003cbr\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003cbr\u003e\u003c\/div\u003e\u003cdiv\u003eAfter you read this book, you will have the fundamentals to become proficient in using Apache Spark and know when and how to apply it to your big data applications.  \u003c\/div\u003e\u003cdiv\u003e\u003cbr\u003e\u003c\/div\u003e\u003cdiv\u003e\u003cbr\u003e\u003c\/div\u003e\u003cdiv\u003e\u003cb\u003eWhat You Will Learn  \u003c\/b\u003e\u003c\/div\u003e\u003cdiv\u003e\u003cul\u003e\n\u003cli\u003eUnderstand Spark unified data processing platform\u003c\/li\u003e\n\u003cli\u003eHow\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/div\u003e","brand":"APress","offers":[{"title":"Default Title","offer_id":48885824422231,"sku":"9781484235782","price":26.39,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781484235782.jpg?v=1722537827"},{"product_id":"leveling-up-with-sql-9781484296844","title":"Leveling Up with SQL","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eIntermediate-Advanced user level\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eChapter 1:  Getting Ready.- Chapter 2:  Working with Table Design.- Chapter 3:  Table Relationships and Working With Joins.- Chapter 4:  Working with Calculated Data.- Chapter 5:  Aggregating Data.- Chapter 6:  Creating and Using Views and Friends.- Chapter 7:  Working With Subqueries and Common Table Expressions.- Chapter 8:  Working With Window Functions.-Chapter 9: More on Common Table Expressions.- Chapter 10: More Techniques with SQL: Triggers, Pivot Tables, and Variables.- Appendix A.\u003c\/p\u003e","brand":"APress","offers":[{"title":"Default Title","offer_id":48885834154327,"sku":"9781484296844","price":33.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781484296844.jpg?v=1722537862"},{"product_id":"practical-ai-for-business-leaders-product-managers-and-entrepreneurs-9781501514647","title":"Practical AI for Business Leaders Product","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eMost economists agree that AI is a general purpose technology (GPT) like the steam engine, electricity, and the computer. AI will drive innovation in all sectors of the economy for the foreseeable future. Practical AI for Business Leaders, Product Managers, and Entrepreneurs is a technical guidebook for the business leader or anyone responsible for leading AI-related initiatives in their organization. The book can also be used as a foundation to explore the ethical implications of AI.    Authors Alfred Essa and Shirin Mojarad provide a gentle introduction to foundational topics in AI. Each topic is framed as a triad: concept, theory, and practice. The concept chapters develop the intuition, culminating in a practical case study. The theory chapters reveal the underlying technical machinery. The practice chapters provide code in Python to implement the models discussed in the case study.   With this book, readers will learn:     The technical foundations of machine learning and deep lea\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e Introduction \u003c\/p\u003e \u003cp\u003e What is AI and why it is at the center of major business transformation? \u003c\/p\u003e \u003cp\u003e How is it related to machine learning? \u003c\/p\u003e \u003cp\u003e What is deep learning, and how is it related to ML? \u003c\/p\u003e \u003cp\u003e Why is it important? \u003c\/p\u003e \u003cp\u003e How the book is organized \u003c\/p\u003e \u003cp\u003e Who is the audience? \u003c\/p\u003e \u003cp\u003e \u003cbr\u003e\u003cstrong\u003eSection 1: Machine Learning　\u003c\/strong\u003eChapter 1.1, introduction, machine learning, different types of machine learning　 \u003c\/p\u003e \u003cp\u003e Chapter 1.2,　\u003cem\u003eMachine Learning Technical Overview　\u003c\/em\u003e \u003c\/p\u003e \u003cp\u003e Chapter 1.3, \u003cem\u003eHands-On Machine Learning with Scikit Learn\u003c\/em\u003e \u003c\/p\u003e \u003cp\u003e Chapter 1.4,　　\u003cem\u003eAdvanced Topics\/flavors of Machine learning\u003c\/em\u003e \u003c\/p\u003e \u003cp\u003e Appendix: mathematical interlude \u003c\/p\u003e \u003cp\u003e \u003cbr\u003e \u003c\/p\u003e \u003cstrong\u003e\u003c\/strong\u003e \u003cp\u003e Section 2: Deep Learning　 \u003c\/p\u003e \u003cp\u003e Chapter 2.1, introduction (what is it, why is it important) \u003c\/p\u003e \u003cp\u003e Chapter 2.2,　\u003cem\u003eDeep Learning Technical Overview　\u003c\/em\u003e \u003c\/p\u003e \u003cp\u003e Chapter 2.3, \u003cem\u003eHands-On Deep Learning with Keras\u003c\/em\u003e \u003c\/p\u003e \u003cp\u003e Chapter 2.4,　　\u003cem\u003eAdvanced Topics\/flavors of deep learning\u003c\/em\u003e \u003c\/p\u003e \u003cp\u003e Appendix: mathematical interlude \u003c\/p\u003e \u003cp\u003e \u003cbr\u003e \u003c\/p\u003e \u003cstrong\u003e\u003c\/strong\u003e \u003cp\u003e Section 3: Putting AI into Practice: Innovation Framework \u003c\/p\u003e \u003cp\u003e Chapter 3.1: Diffusion and Dynamics of Innovation \u003c\/p\u003e \u003cp\u003e Chapter 3.2: Managing an Innovation Portfolio \u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e","brand":"De Gruyter","offers":[{"title":"Default Title","offer_id":48885907161431,"sku":"9781501514647","price":40.95,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781501514647.jpg?v=1722538118"},{"product_id":"building-an-effective-security-program-9781501515248","title":"Building an Effective Security Program","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eBuilding an Effective Security Program provides readers with a comprehensive approach to securing the IT systems in use at their organizations. This book provides information on how to structure and operate an effective cybersecurity program that includes people, processes, technologies, security awareness, and training. This program will establish and maintain effective security protections for the confidentiality, availability, and integrity of organization information. In this book, the authors take a pragmatic approach to building organization cyberdefenses that are effective while also remaining affordable.  This book is intended for business leaders, IT professionals, cybersecurity personnel, educators, and students interested in deploying real-world cyberdefenses against today''s persistent and sometimes devastating cyberattacks. It includes detailed explanation of the following IT security topics:   IT Security Mindset-Think like an IT security professional, and consider how yo\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eFOREWORD – 1 page \u003c\/p\u003e\u003cstrong\u003e\u003c\/strong\u003e \u003cp\u003eABOUT THE AUTHORS – 1 page \u003c\/p\u003e\u003cstrong\u003e\u003c\/strong\u003e \u003cp\u003eACKNOWLEDGMENTS – 1 page \u003c\/p\u003e\u003cstrong\u003e\u003c\/strong\u003e \u003cp\u003eINTRODUCTION – 2 pages \u003c\/p\u003e \u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eWhat is this book about? \u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eWho should read this book? \u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eWhy did the authors write this book? \u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eOrganization of the book \u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003cstrong\u003e\u003c\/strong\u003e \u003cp\u003eCHAPTERS \u003c\/p\u003e \u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eChapter 1—Business Case (~15 pages) \u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eThis chapter presents the business case for setting up an enduring IT security awareness and training program for use in training the employees of the company—from IT users to career IT security professionals. This chapter introduces fundamental concepts and terms used throughout the book. \u003c\/p\u003e \u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003c\/strong\u003e \u003cp\u003e\u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eChapter 2—IT Security Mind Set (~15 pages) \u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eThis chapter presents thinking like an IT security professional to establish and maintain common security protections. \u003c\/p\u003e \u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003c\/strong\u003e \u003cp\u003e\u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eChapter 3—IT Security Risk Management (~15 pages) \u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eThis chapter presents a risk management process that involves asset management, security vulnerabilities, security threats, risk identification, risk mitigation, and security controls. \u003c\/p\u003e \u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003c\/strong\u003e \u003cp\u003e\u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eChapter 4—IT Security Process (~15 pages) \u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eThis chapter presents how to establish security scopes and select corresponding controls to protect the confidentiality, availability, and integrity of company information. \u003c\/p\u003e \u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003c\/strong\u003e \u003cp\u003e\u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eChapter 5—IT Security Scenarios and Perspectives (~40 pages) \u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eThis chapter presents how the Chapter 4 IT security process is applied to various scenarios. Each scenario will walk through a number of common security controls and apply the IT security process to identify how to protect company information. \u003c\/p\u003e \u003col\u003e\u003cli\u003e\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eIT security at home \u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eIT security while traveling \u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eIT security at work \u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eIT security as an executive \u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eInternational IT security \u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ol\u003e\u003c\/li\u003e\u003c\/ol\u003e \u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003c\/strong\u003e \u003cp\u003e\u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eChapter 6—Planning IT Security Awareness and Training (~15 pages) \u003cstrong\u003e\u003c\/strong\u003e\n\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eThis chapter presents practical guidance on how to write an IT Awareness and Training implementation plan. \u003c\/p\u003e \u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003c\/strong\u003e \u003cp\u003e\u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eChapter 7—Implementing IT Security Awareness and Training Program(~15 pages) \u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eThis chapter presents human issues related to bringing about enterprise-wide cultural change due to implementation of an IT Awareness and Training Program. \u003c\/p\u003e \u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003c\/strong\u003e \u003cp\u003e\u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eChapter 8—Measuring IT Security Awareness and Training Program Implementation (~15 pages) \u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eThis chapter presents practical guidance for measuring program implementation success and how to use the measurements to achieve awareness and training goals. \u003c\/p\u003e \u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003c\/strong\u003e \u003cp\u003e\u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eChapter 9—Managing Continual Program Improvement (~15 pages) \u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eThis chapter presents practical guidance for monitoring compliance, evaluating feedback and improving the program. \u003c\/p\u003e \u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003c\/strong\u003e \u003cp\u003e\u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eChapter 10—Looking to the Future (~15 pages) \u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eThis chapter presents a view of the evolving cybersecurity attacks as they become more capable and sophisticated. \u003c\/p\u003e\u003cstrong\u003e\u003c\/strong\u003e \u003cp\u003eAPPENDICES – 10 pages \u003c\/p\u003e\u003cstrong\u003e\u003c\/strong\u003e \u003cp\u003eGLOSSARY – 3 pages \u003c\/p\u003e\u003cstrong\u003e\u003c\/strong\u003e \u003cp\u003eBIBLIOGRAPHY – 3 pages \u003c\/p\u003e\u003cstrong\u003e\u003c\/strong\u003e \u003cp\u003eINDEX – 4 pages \u003c\/p\u003e","brand":"De Gruyter","offers":[{"title":"Default Title","offer_id":48885907718487,"sku":"9781501515248","price":43.2,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781501515248.jpg?v=1722538119"},{"product_id":"medical-knowledge-extraction-from-big-data-9781536179255","title":"Medical Knowledge Extraction from Big Data","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eData mining refers to the activity of going through big data sets to look for relevant information. As human health care data are the most difficult of all data to collect and their primary direction is the treatment of patients, and secondarily dealing with research, almost the only vindication for collecting medical data is to benefit the disease. All data miners should take into account that Medical Knowledge Extraction is internally connected with the Evidence-Based Medical approach because it uses data for already treated or not patients and there are times that opposites to Guideline Based medical practice. Additonally all researchers should be aware when are dealing with medical databases they may face the possibility that their work will never be accepted or even used from health care professionals if all these obligations will not be correctly addressed from the early beginning. In the present book, one can find after the three introductory chapters, a number of successfully evaluated applications that have been developed after mining approaches in Big or smaller amount (according to the application) of medical Data in different fields of every day clinical practice from teams of experts. The challenging adventure of Medical Knowledge Extraction can be followed by ambitious researchers finally resulting in a successful decision support system, that some times is so novel that it will provide new directions for basic or clinical research further that the existed. At least this procedure will save the experience of the best doctors on duty and will help young residents to be better and better.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eFor more information, please visit our website at:https:\/\/novapublishers.com\/shop\/medical-knowledge-extraction-from-big-data\/","brand":"Nova Science Publishers Inc","offers":[{"title":"Default Title","offer_id":48886172254551,"sku":"9781536179255","price":113.59,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781536179255.jpg?v=1722539080"},{"product_id":"quick-start-guide-to-azure-data-factory-azure-data-lake-server-and-azure-data-warehouse-9781547417353","title":"Quick Start Guide to Azure Data Factory, Azure","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eWith constantly expanding options such as Azure Data Lake Server (ADLS) and Azure SQL Data Warehouse (ADW), how can developers learn the process and components required to successfully move this data? Quick Start Guide to Azure Data Factory, Azure Data Lake Server, and Azure Data Warehouse teaches you the basics of moving data between Azure SQL solutions using Azure Data Factory. Discover how to build and deploy each of the components needed to integrate data in the cloud with local SQL databases.       Mark Beckner's step by step instructions on how to build each component, how to test processes and debug, and how to track and audit the movement of data, will help you to build your own solutions instantly and efficiently. This book includes information on configuration, development, and administration of a fully functional solution and outlines all of the components required for moving data from a local SQL instance through to a fully functional data warehouse with facts and dimensions.","brand":"De Gruyter","offers":[{"title":"Default Title","offer_id":48886243656023,"sku":"9781547417353","price":16.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781547417353.jpg?v=1722539337"},{"product_id":"graph-databases-in-action-9781617296376","title":"Graph Databases in Action","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003ci\u003eGraph Databases in Action\u003c\/i\u003e teaches readers everything they need to know to begin building and running applications powered by graph databases. Right off the bat, seasoned graph database experts introduce readers to just enough graph theory, the graph database ecosystem, and a variety of datastores. They also explore modelling basics in action with real-world examples, then go hands-on with querying, coding traversals, parsing results, and other essential tasks as readers build their own graph-backed social network app complete with a recommendation engine!\u003c\/p\u003e \u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKey Features\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e·   Graph database fundamentals\u003c\/p\u003e \u003cp\u003e·   An overview of the graph database ecosystem\u003c\/p\u003e \u003cp\u003e·   Relational vs. graph database modelling\u003c\/p\u003e \u003cp\u003e·   Querying graphs using Gremlin\u003c\/p\u003e \u003cp\u003e·   Real-world common graph use cases\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eFor readers with basic Java and application development skills building in RDBMS systems such as Oracle, SQL Server, MySQL, and Postgres. No experience with graph databases is required.\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003eAbout the technology \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGraph databases store interconnected data in a more natural form, making them superior tools for representing data with rich relationships. Unlike in relational database management systems (RDBMS), where a more rigid view of data connections results in the loss of valuable insights, in graph databases, data connections are first priority.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003eDave Bechberger\u003c\/b\u003e has extensive experience using graph databases as a product architect and a consultant. He’s spent his career leveraging cutting-edge technologies to build software in complex data domains such as bioinformatics, oil and gas, and supply chain management. He’s an active member of the graph community and has presented on a wide variety of graph-related topics at national and international conferences.\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003eJosh Perryman\u003c\/b\u003e is technologist with over two decades of diverse experience building and maintaining complex systems, including high performance computing (HPC) environments. Since 2014 he has focused on graph databases, especially in distributed or big data environments, and he regularly blogs and speaks at conferences about graph databases.\u003c\/p\u003e","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":48886899769687,"sku":"9781617296376","price":37.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781617296376.jpg?v=1722542086"},{"product_id":"demand-forecasting-best-practices-9781633438095","title":"Demand Forecasting Best Practices","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eMaster the demand forecasting skills you need to decide what resources to acquire, products to produce, and where and how to distribute them. \u003cp\u003eFor demand planners, S\u0026amp;OP managers, supply chain leaders, and data scientists. \u003cstrong\u003eDemand Forecasting Best Practices\u003c\/strong\u003e is a unique step-by-step guide, demonstrating forecasting tools, metrics, and models alongside stakeholder management techniques that work in a live business environment.\u003c\/p\u003e \u003cp\u003eYou will learn how to:\u003c\/p\u003e \u003cul\u003e\n\u003cli\u003eLead a demand planning team to improve forecasting quality while reducing workload\u003c\/li\u003e\n\u003cli\u003eProperly define the objectives, granularity, and horizon of your demand planning process\u003c\/li\u003e\n\u003cli\u003eUse smart, value-weighted KPIs to track accuracy and bias\u003c\/li\u003e\n\u003cli\u003eSpot areas of your process where there is room for improvement\u003c\/li\u003e\n\u003cli\u003eHelp planners and stakeholders (sales, marketing, finances) add value to your process\u003c\/li\u003e\n\u003cli\u003eIdentify what kind of data you should be collecting, and how\u003c\/li\u003e\n\u003cli\u003eUtilise different types of statistical and machine learning models\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eFollow author Nicolas Vandeput's original five-step framework for demand planning excellence and learn how to tailor it to your own company's needs. You will learn how to optimise demand planning for a more effective supply chain and will soon be delivering accurate predictions that drive major business value.\u003c\/p\u003e About the technology \u003cp\u003eDemand forecasting is vital for the success of any product supply chain. It allows companies to make better decisions about what resources to acquire, what products to produce, and where and how to distribute them. As an effective demand forecaster, you can help your organisation avoid overproduction, reduce waste, and optimise inventory levels for a real competitive advantage.\u003c\/p\u003e","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":48887150182743,"sku":"9781633438095","price":41.72,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781633438095.jpg?v=1722543242"},{"product_id":"analytics-how-to-win-with-intelligence-9781634622370","title":"Analytics: How to Win with Intelligence","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eLearn how big data and other sources of information can be transformed into valuable knowledge -- knowledge that can create incredible competitive advantage to propel a business toward market leadership. Learn through examples and experience exactly how to pick projects and build analytics teams that deliver results. Know the ethical and privacy issues, and apply the three-part litmus test of context, permission, and accuracy. Without a doubt, data and analytics are the new source of competitive advantage, but how do executives go from hype to action? Thats the objective of this book -- to assist executives in making the right investments in the right place and at the right time in order to reap the full benefits of data analytics.","brand":"Technics Publications LLC","offers":[{"title":"Default Title","offer_id":48887162274135,"sku":"9781634622370","price":27.89,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781634622370.jpg?v=1722543305"},{"product_id":"data-mining-principles-applications-emerging-challenges-9781634637381","title":"Data Mining: Principles, Applications \u0026 Emerging","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"Nova Science Publishers Inc","offers":[{"title":"Default Title","offer_id":48887177150807,"sku":"9781634637381","price":127.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781634637381.jpg?v=1722543367"}],"url":"https:\/\/bookcurl.com\/collections\/data-mining.oembed?page=5","provider":"Book Curl","version":"1.0","type":"link"}