Data mining Books

Book SynopsisWith 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.

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Book SynopsisRadio 4's Book of the WeekA Financial Times Book of the YearShortlisted for the 2020 Financial Times / McKinsey Business Book of the YearLonglisted for the National Book Award '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 RuncimanThe 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. Borrowing 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 New York Times, Young & Rubicam, and, during the Vietnam War, the Department of Defence. In If Then, distinguished Harvard historian and New Yorker staff 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.'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 Lincoln in the Bardo'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'If Then is simultaneously gripping and absolutely terrifying' Amanda ForemanTrade ReviewLepore is a brilliant writer. It's a dream to read. -- Diane CoyleIf 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. * Financial Times *Jill 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 Mad Men with Theranos. -- David RuncimanFascinating. * New York Times Book Review *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 SaundersJill Lepore writes history like a poet. In If Then 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. -- Dan SnowTwo 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. -- Frances Cairncross * The Literary Review *Beautifully written and intellectually rigorous account of the origins of the science of predictive analytics and behavioral data science in the cold war era. * Financial Times *Fascinating. -- Amol Rajan * Start the Week *Everything Lepore writes is distinguished by intelligence, eloquence, and fresh insight. If Then 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. -- Susan OrleanA wonderfully written history of long-forgotten computer group Simulmatics. * Financial Times *

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Book SynopsisThomas Erl is a top-selling IT author, founder of Arcitura Education and series editor of the Prentice Hall Service Technology Series from Thomas Erl. With more than 200,000 copies in print worldwide, his books have become international bestsellers and have been formally endorsed by senior members of major IT organizations, such as IBM, Microsoft, Oracle, Intel, Accenture, IEEE, HL7, MITRE, SAP, CISCO, HP and many others. As CEO of Arcitura Education Inc., Thomas has led the development of curricula for the internationally recognized Big Data Science Certified Professional (BDSCP), Cloud Certified Professional (CCP) and SOA Certified Professional (SOACP) accreditation programs, which have established a series of formal, vendor-neutral industry certifications obtained by thousands of IT professionals around the world. Thomas has toured more than 20 countries as a speaker and instructor. More than 100 articles and interviews by Thomas have been published in numerous publicaTable of ContentsAcknowledgments xviiReader Services xviiiPART I: THE FUNDAMENTALS OF BIG DATAChapter 1: Understanding Big Data 3 Concepts and Terminology 5 Datasets 5 Data Analysis 6 Data Analytics 6 Descriptive Analytics 8 Diagnostic Analytics 9 Predictive Analytics 10 Prescriptive Analytics 11 Business Intelligence (BI) 12 Key Performance Indicators (KPI) 12 Big Data Characteristics 13 Volume 14 Velocity 14 Variety 15 Veracity 16 Value 16 Different Types of Data 17 Structured Data 18 Unstructured Data 19 Semi-structured Data 19 Metadata 20 Case Study Background 20 History 20 Technical Infrastructure and Automation Environment 21 Business Goals and Obstacles 22 Case Study Example 24 Identifying Data Characteristics 26 Volume 26 Velocity 26 Variety 26 Veracity 26 Value 27 Identifying Types of Data 27 Chapter 2: Business Motivations and Drivers for Big Data Adoption 29 Marketplace Dynamics 30 Business Architecture 33 Business Process Management 36 Information and Communications Technology 37 Data Analytics and Data Science 37 Digitization 38 Affordable Technology and Commodity Hardware 38 Social Media 39 Hyper-Connected Communities and Devices 40 Cloud Computing 40 Internet of Everything (IoE) 42 Case Study Example 43 Chapter 3: Big Data Adoption and Planning Considerations 47 Organization Prerequisites 49 Data Procurement 49 Privacy 49 Security 50 Provenance 51 Limited Realtime Support 52 Distinct Performance Challenges 53 Distinct Governance Requirements 53 Distinct Methodology 53 Clouds 54 Big Data Analytics Lifecycle 55 Business Case Evaluation 56 Data Identification 57 Data Acquisition and Filtering 58 Data Extraction 60 Data Validation and Cleansing 62 Data Aggregation and Representation 64 Data Analysis 66 Data Visualization 68 Utilization of Analysis Results 69 Case Study Example 71 Big Data Analytics Lifecycle 73 Business Case Evaluation 73 Data Identification 74 Data Acquisition and Filtering 74 Data Extraction 74 Data Validation and Cleansing 75 Data Aggregation and Representation 75 Data Analysis 75 Data Visualization 76 Utilization of Analysis Results 76 Chapter 4: Enterprise Technologies and Big Data Business Intelligence 77 Online Transaction Processing (OLTP) 78 Online Analytical Processing (OLAP) 79 Extract Transform Load (ETL) 79 Data Warehouses 80 Data Marts 81 Traditional BI 82 Ad-hoc Reports 82 Dashboards 82 Big Data BI 84 Traditional Data Visualization 84 Data Visualization for Big Data 85 Case Study Example 86 Enterprise Technology 86 Big Data Business Intelligence 87 PART II: STORING AND ANALYZING BIG DATAChapter 5: Big Data Storage Concepts 91 Clusters 93 File Systems and Distributed File Systems 93 NoSQL 94 Sharding 95 Replication 97 Master-Slave 98 Peer-to-Peer 100 Sharding and Replication 103 Combining Sharding and Master-Slave Replication 104 Combining Sharding and Peer-to-Peer Replication 105 CAP Theorem 106 ACID 108 BASE 113 Case Study Example 117 Chapter 6: Big Data Processing Concepts 119 Parallel Data Processing 120 Distributed Data Processing 121 Hadoop 122 Processing Workloads 122 Batch 123 Transactional 123 Cluster 124 Processing in Batch Mode 125 Batch Processing with MapReduce 125 Map and Reduce Tasks 126 Map 127 Combine 127 Partition 129 Shuffle and Sort 130 Reduce 131 A Simple MapReduce Example 133 Understanding MapReduce Algorithms 134 Processing in Realtime Mode 137 Speed Consistency Volume (SCV) 137 Event Stream Processing 140 Complex Event Processing 141 Realtime Big Data Processing and SCV 141 Realtime Big Data Processing and MapReduce 142 Case Study Example 143 Processing Workloads 143 Processing in Batch Mode 143 Processing in Realtime 144 Chapter 7: Big Data Storage Technology 145 On-Disk Storage Devices 147 Distributed File Systems 147 RDBMS Databases 149 NoSQL Databases 152 Characteristics 152 Rationale 153 Types 154 Key-Value 156 Document 157 Column-Family 159 Graph 160 NewSQL Databases 163 In-Memory Storage Devices 163 In-Memory Data Grids 166 Read-through 170 Write-through 170 Write-behind 172 Refresh-ahead 172 In-Memory Databases 175 Case Study Example 179 Chapter 8: Big Data Analysis Techniques 181 Quantitative Analysis 183 Qualitative Analysis 184 Data Mining 184 Statistical Analysis 184 A/B Testing 185 Correlation 186 Regression 188 Machine Learning 190 Classification (Supervised Machine Learning) 190 Clustering (Unsupervised Machine Learning) 191 Outlier Detection 192 Filtering 193 Semantic Analysis 195 Natural Language Processing 195 Text Analytics 196 Sentiment Analysis 197 Visual Analysis 198 Heat Maps 198 Time Series Plots 200 Network Graphs 201 Spatial Data Mapping 202 Case Study Example 204 Correlation 204 Regression 204 Time Series Plot 205 Clustering 205 Classification 205 Appendix A: Case Study Conclusion 207About the Authors 211 Thomas Erl 211 Wajid Khattak 211 Paul Buhler 212 Index 213

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Book SynopsisWith Scraping Social Media you'll learn how to find out what kind of data is available on popular social media juggernauts like Facebook and Twitter and how to recognise the value of what is measured. Practical exercises interweave with conceptual lessons that cover ways to use Python to extract data from social media sources, analyze it, and make sense of it visually. You'll learn how to write a script that taps into an API, how to scrape data from websites, and even how to make sense of emoji usage in your data.Trade Review"If you want to know a little bit about Data Science while learning Python along the way, Mining Social Media is a must read . . . It's a fun and hands on approach to the topic, and I'd love to have read this when I was starting coding!"—Gonçalo Palma, @GonPalma"Excellently written, with complex topics made easy to understand, and has a welcoming style of prose."—Ryan K. Louie, MD, PhD, @ryanlouie"If you haven't read Lam's book, Mining Social Media, trust us — you're gonna dig it." —Craig Newmark Graduate School of Journalism, @newmarkjschoolTable of ContentsIntroductionPart I: Data MiningChapter 1: The Programming Languages You’ll Need to KnowChapter 2: Where to Get Your DataChapter 3: Getting Data with CodeChapter 4: Scraping Your Own Facebook DataChapter 5: Scraping a Live SitePart II: Data AnalysisChapter 6: Introduction to Data AnalysisChapter 7: Visualizing Your DataChapter 8: Advanced Tools for Data AnalysisChapter 9: Finding Trends in Reddit DataChapter 10: Measuring the Twitter Activity of Political ActorsChapter 11: Where to Go from Here

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Book SynopsisDas kompakte Fachbuch gibt einen Überblick über die Möglichkeiten von „Big Data“ im Gesundheitswesen und beschreibt anhand von ausgewählten Szenarien mögliche Einsatzgebiete.Die 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.Ein 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. Table of ContentsBig-Data-Analytics im Gesundheitswesen - Medizin - Verwaltung - Forschung: Anwendungsgebiete für Big-Data-Analytics - Gesetzliche Rahmenbedingungen und Big-Data-Ethik

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Book SynopsisTrade Review"[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."---Paul Cuffe, IEEE Transactions"Undoubtedly, this book is an excellent introduction to an essential tool for anyone who needs to collect and present data." * Conservation Biology *

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Book SynopsisWith this updated second edition, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.

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Book SynopsisBuild predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting. In Time Series Forecasting in Python you will learn how to: Recognize a time series forecasting problem and build a performant predictive model Create univariate forecasting models that account for seasonal effects and external variables Build multivariate forecasting models to predict many time series at once Leverage large datasets by using deep learning for forecasting time series Automate the forecasting process DESCRIPTION Time Series Forecasting in Python 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. Time Series Forecasting in Python 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 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. Test your skills with hands-on projects for forecasting air travel, volume of drug prescriptions, and the earnings of Johnson & Johnson. By the time you're done, you'll be ready to build accurate and insightful forecasting models with tools from the Python ecosystem.Table of Contentstable 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

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Book SynopsisMagnus Ekman, Ph.D., is a director of architecture at NVIDIA Corporation. His doctorate is in computer engineering, and he is the inventor of multiple patents. He was first exposed to artificial neural networks in the late nineties in his native country, Sweden. After some dabbling in evolutionary computation, he ended up focusing on computer architecture and relocated to Silicon Valley, where he lives with his wife Jennifer, children Sebastian and Sofia, and dog Babette. He previously worked with processor design and R&D at Sun Microsystems and Samsung Research America, and has been involved in starting two companies, one of which (Skout) was later acquired by The Meet Group, Inc. In his current role at NVIDIA, he leads an engineering team working on CPU performance and power efficiency for system on chips targeting the autonomous vehicle market. As the Deep Learning (DL) field exploded the past few years, fueled by NVIDIA's GPU technology and CUDA, Dr. Ekman fTable of ContentsForeword by Dr. Anima Anandkumar xxiForeword by Dr. Craig Clawson xxiiiPreface xxvAcknowledgments liAbout the Author liii Chapter 1: The Rosenblatt Perceptron 1 Example of a Two-Input Perceptron 4 The Perceptron Learning Algorithm 7 Limitations of the Perceptron 15 Combining Multiple Perceptrons 17 Implementing Perceptrons with Linear Algebra 20 Geometric Interpretation of the Perceptron 30 Understanding the Bias Term 33 Concluding Remarks on the Perceptron 34 Chapter 2: Gradient-Based Learning 37 Intuitive Explanation of the Perceptron Learning Algorithm 37 Derivatives and Optimization Problems 41 Solving a Learning Problem with Gradient Descent 44 Constants and Variables in a Network 48 Analytic Explanation of the Perceptron Learning Algorithm 49 Geometric Description of the Perceptron Learning Algorithm 51 Revisiting Different Types of Perceptron Plots 52 Using a Perceptron to Identify Patterns 54 Concluding Remarks on Gradient-Based Learning 57 Chapter 3: Sigmoid Neurons and Backpropagation 59 Modified Neurons to Enable Gradient Descent for Multilevel Networks 60 Which Activation Function Should We Use? 66 Function Composition and the Chain Rule 67 Using Backpropagation to Compute the Gradient 69 Backpropagation with Multiple Neurons per Layer 81 Programming Example: Learning the XOR Function 82 Network Architectures 87 Concluding Remarks on Backpropagation 89 Chapter 4: Fully Connected Networks Applied to Multiclass Classification 91 Introduction to Datasets Used When Training Networks 92 Training and Inference 100 Extending the Network and Learning Algorithm to Do Multiclass Classification 101 Network for Digit Classification 102 Loss Function for Multiclass Classification 103 Programming Example: Classifying Handwritten Digits 104 Mini-Batch Gradient Descent 114 Concluding Remarks on Multiclass Classification 115 Chapter 5: Toward DL: Frameworks and Network Tweaks 117 Programming Example: Moving to a DL Framework 118 The Problem of Saturated Neurons and Vanishing Gradients 124 Initialization and Normalization Techniques to Avoid Saturated Neurons 126 Cross-Entropy Loss Function to Mitigate Effect of Saturated Output Neurons 130 Different Activation Functions to Avoid Vanishing Gradient in Hidden Layers 136 Variations on Gradient Descent to Improve Learning 141 Experiment: Tweaking Network and Learning Parameters 143 Hyperparameter Tuning and Cross-Validation 146 Concluding Remarks on the Path Toward Deep Learning 150 Chapter 6: Fully Connected Networks Applied to Regression 153 Output Units 154 The Boston Housing Dataset 160 Programming Example: Predicting House Prices with a DNN 161 Improving Generalization with Regularization 166 Experiment: Deeper and Regularized Models for House Price Prediction 169 Concluding Remarks on Output Units and Regression Problems 170 Chapter 7: Convolutional Neural Networks Applied to Image Classification 171 The CIFAR-10 Dataset 173 Characteristics and Building Blocks for Convolutional Layers 175 Combining Feature Maps into a Convolutional Layer 180 Combining Convolutional and Fully Connected Layers into a Network 181 Effects of Sparse Connections and Weight Sharing 185 Programming Example: Image Classification with a Convolutional Network 190 Concluding Remarks on Convolutional Networks 201 Chapter 8: Deeper CNNs and Pretrained Models 205 VGGNet 206 GoogLeNet 210 ResNet 215 Programming Example: Use a Pretrained ResNet Implementation 223 Transfer Learning 226 Backpropagation for CNN and Pooling 228 Data Augmentation as a Regularization Technique 229 Mistakes Made by CNNs 231 Reducing Parameters with Depthwise Separable Convolutions 232 Striking the Right Network Design Balance with EfficientNet 234 Concluding Remarks on Deeper CNNs 235 Chapter 9: Predicting Time Sequences with Recurrent Neural Networks 237 Limitations of Feedforward Networks 241 Recurrent Neural Networks 242 Mathematical Representation of a Recurrent Layer 243 Combining Layers into an RNN 245 Alternative View of RNN and Unrolling in Time 246 Backpropagation Through Time 248 Programming Example: Forecasting Book Sales 250 Dataset Considerations for RNNs 264 Concluding Remarks on RNNs 265 Chapter 10: Long Short-Term Memory 267 Keeping Gradients Healthy 267 Introduction to LSTM 272 LSTM Activation Functions 277 Creating a Network of LSTM Cells 278 Alternative View of LSTM 280 Related Topics: Highway Networks and Skip Connections 282 Concluding Remarks on LSTM 282 Chapter 11: Text Autocompletion with LSTM and Beam Search 285 Encoding Text 285 Longer-Term Prediction and Autoregressive Models 287 Beam Search 289 Programming Example: Using LSTM for Text Autocompletion 291 Bidirectional RNNs 298 Different Combinations of Input and Output Sequences 300 Concluding Remarks on Text Autocompletion with LSTM 302 Chapter 12: Neural Language Models and Word Embeddings 303 Introduction to Language Models and Their Use Cases 304 Examples of Different Language Models 307 Benefit of Word Embeddings and Insight into How They Work 313 Word Embeddings Created by Neural Language Models 315 Programming Example: Neural Language Model and Resulting Embeddings 319 King − Man + Woman! = Queen 329 King − Man + Woman ! = Queen 331 Language Models, Word Embeddings, and Human Biases 332 Related Topic: Sentiment Analysis of Text 334 Concluding Remarks on Language Models and Word Embeddings 342 Chapter 13: Word Embeddings from word2vec and GloVe 343 Using word2vec to Create Word Embeddings Without a Language Model 344 Additional Thoughts on word2vec 352 word2vec in Matrix Form 353 Wrapping Up word2vec 354 Programming Example: Exploring Properties of GloVe Embeddings 356 Concluding Remarks on word2vec and GloVe 361 Chapter 14: Sequence-to-Sequence Networks and Natural Language Translation 363 Encoder-Decoder Model for Sequence-to-Sequence Learning 366 Introduction to the Keras Functional API 368 Programming Example: Neural Machine Translation 371 Experimental Results 387 Properties of the Intermediate Representation 389 Concluding Remarks on Language Translation 391 Chapter 15: Attention and the Transformer 393 Rationale Behind Attention 394 Attention in Sequence-to-Sequence Networks 395 Alternatives to Recurrent Networks 406 Self-Attention 407 Multi-head Attention 410 The Transformer 411 Concluding Remarks on the Transformer 415 Chapter 16: One-to-Many Network for Image Captioning 417 Extending the Image Captioning Network with Attention 420 Programming Example: Attention-Based Image Captioning 421 Concluding Remarks on Image Captioning 443 Chapter 17: Medley of Additional Topics 447 Autoencoders 448 Multimodal Learning 459 Multitask Learning 469 Process for Tuning a Network 477 Neural Architecture Search 482 Concluding Remarks 502 Chapter 18: Summary and Next Steps 503 Things You Should Know by Now 503 Ethical AI and Data Ethics 505 Things You Do Not Yet Know 512 Next Steps 516 Appendix A: Linear Regression and Linear Classifiers 519 Linear Regression as a Machine Learning Algorithm 519 Computing Linear Regression Coefficients 523 Classification with Logistic Regression 525 Classifying XOR with a Linear Classifier 528 Classification with Support Vector Machines 531 Evaluation Metrics for a Binary Classifier 533 Appendix B: Object Detection and Segmentation 539 Object Detection 540 Semantic Segmentation 549 Instance Segmentation with Mask R-CNN 559 Appendix C: Word Embeddings Beyond word2vec and GloVe 563 Wordpieces 564 FastText 566 Character-Based Method 567 ELMo 572 Related Work 575 Appendix D: GPT, BERT, AND RoBERTa 577 GPT 578 BERT 582 RoBERTa 586 Historical Work Leading Up to GPT and BERT 588 Other Models Based on the Transformer 590 Appendix E: Newton-Raphson versus Gradient Descent 593 Newton-Raphson Root-Finding Method 594 Relationship Between Newton-Raphson and Gradient Descent 597 Appendix F: Matrix Implementation of Digit Classification Network 599 Single Matrix 599 Mini-Batch Implementation 602 Appendix G: Relating Convolutional Layers to Mathematical Convolution 607Appendix H: Gated Recurrent Units 613 Alternative GRU Implementation 616 Network Based on the GRU 616 Appendix I: Setting up a Development Environment 621 Python 622 Programming Environment 623 Programming Examples 624 Datasets 625 Installing a DL Framework 628 TensorFlow Specific Considerations 630 Key Differences Between PyTorch and TensorFlow 631 Appendix J: Cheat Sheets 637 Works Cited 647Index 667

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Book SynopsisOverview 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.Trade ReviewFrom the reviews:"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, Technometrics, August 2009, VOL. 51, NO. 3)From the reviews of the second edition:"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)“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)“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)Table of ContentsIntroduction.- 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.

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Book SynopsisThis is the first comprehensive overview of the exciting field of the 'science of science'. With anecdotes and detailed, easy-to-follow explanations of the research, this book is accessible to all scientists, policy makers, and administrators with an interest in the wider scientific enterprise.Trade Review'Wang and Barabási book is a manifesto for the science of science domain. Graduate students (as well as their mentors) owe the authors a debt of gratitude for this impressive synthesis of what is a fast-evolving field of research.' Pierre Azoulay, Massachusetts Institute of Technology'Analyzing quantitative aspects of science with state-of-art tools, Wang and Barabási have written an insightful and comprehensive book that will become a must-read for all scholars interested in science.' Yu Xie, Princeton University'In their engaging book, Wang and Barabási take a fresh look at the science of science. They convincingly argue that in the age of big data and AI applying the scientific method to science itself not only helps understand how science works but may even enhance it. We are compelled to consider the determinants of individual careers and what this means in the age of large-scale scientific collaborations. These and other questions around the meaning of scientific impact, in academia and beyond, make the book highly relevant to scientists, academic administrators and funders alike. By the time the final, forward-looking chapter ends we are hooked on all the correlations and predictions, and so it is only fitting that we are invited to join in, to help shape the field which is likely to be driven by a human-machine collaboration.' Magdalena Skipper, Nature'Overall, I found this book very stimulating. It made me wonder whether in-depth metrics analyses of 'only' the subjective narratives of authors, such as the references list they select, actually creates a foundation on which to form judgement rather than opinion? Namely, what fraction of these publications analysed for their metrics were actually underpinned by their data? As well as provoking thought, this book offers a feast of references, 424 in all. There are such further enticing reads as reference 396, Life3.0: Being Human in the Age of Artificial Intelligence. To conclude, I recommend this book for your library, and maybe even take it for your summer beach reading.' John R. Helliwell, Journal of Applied Crystallography'… a text that should appeal to practicing scientists curious about the structure of the whole scientific enterprise, academic administrators and policy makers interested in evidence-based decision-making, and researchers interested in contributing further to the "science of science." There is no better, handier, and more readable work to appeal to such audiences … Highly recommended.' M. Oromaner, Choice ConnectTable of ContentsIntroduction; Part I. The Science of Career: 1. Productivity of a scientist; 2. The H Index; 3. The Matthew Effect; 4. Age and Scientific Achievement; 5. Random Impact Rule; 6. The Q Factor; 7. Hot Streaks; Part II. The Science of Collaboration: 8. The increasing dominance of teams in science; 9. The Invisible College; 10. Coauthorship Networks; 11. Team Assembly; 12. Small and large teams; 13. Scientific Credit; 14. Credit Allocation; Part III. The Science of Impact: 15. Big Science; 16. Citation Disparity; 17. High Impact Papers; 18. Scientific Impact; 19. The Time Dimension of Science; 20. Ultimate Impact; Part IV. Outlook: 21. Can Science be Accelerated?; 22. Artificial Intelligence; 23. Bias and Causality in Science; Part V. Last thought; All the Science of Science: Appendix A1 Modeling team assembly; Appendix A2 Modeling Citations; References; Index.

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Book SynopsisTable of ContentsChapter 1 Getting Started with Power BI What Is Power BI? 4 Understanding the Different Components of Power BI 6 Understanding Power BI as Part of the Power Platform 7 Install Power BI Desktop 8 Start and Pin Power BI Desktop 10 Explore the Power BI Workspace 12 Chapter 2 Connecting Power BI to Your Data Grasp How Power Query Editor Works with Power BI Desktop 18 Connect Power BI Desktop to a Local File 20 Save, Close, and Open Power BI Reports 22 Start Working with the Sample Dataset 24 Connect to a Power BI Dataset 28 Connect to a SharePoint List 30 Connect to a SQL Server Database 34 Chapter 3 Cleaning and Shaping Data Remove Duplicate Values 40 Replace Values in a Column 42 Split a Column Using a Delimiter 44 Group Data 46 Add a Calculated Column 48 Add an Index Column 50 Chapter 4 Modeling Data in Model View Create Dimension Tables 54 Create Relationships Between Tables 58 Create a Star Schema 62 Create a Hierarchical Schema 64 Using the Properties Pane 70 Chapter 5 Creating Basic Visualizations Create a Bar Chart 74 Apply Filters to Visuals 76 Format the Y-Axis of a Bar Chart 78 Format the X-Axis of a Bar Chart 80 Add and Format the Data Category of a Bar Chart 82 Move a Bar Chart’s Legend and Add Gridlines 84 Add a Zoom Slider and Update Bar Colors 86 Add Data Labels to a Bar Chart 88 Add an Image to the Plot Area Background 90 Create a Line Chart or Area Chart 92 Format the Axes of a Line or Area Chart 94 Add a Legend to a Line or Area Chart 96 Move the Legend and Add Gridlines to a Line or Area Chart 98 Add a Zoom Slider and Steps to a Line or Area Chart 100 Add Data Markers and Labels to a Line or Area Chart 102 Format the Data Labels of a Line or Area Chart 104 Chapter 6 Creating Advanced Data Visualizations Create and Format a Gauge Chart 108 Create and Format a KPI Visual 112 Create a Matrix Visual 116 Format a Matrix Visual 118 Format the Values and Column Headers of a Matrix Visual 120 Format the Row Headers of a Matrix Visual 122 Format the Row Subtotals and Grand Totals of a Matrix Visual 124 Format the Specific Column and Cell Elements of a Matrix Visual 126 Create a Waterfall Chart 128 Format a Waterfall Chart 130 Format the X-Axis and Legend of a Waterfall Chart 132 Add and Format Breakdowns in a Waterfall Chart 134 Create, Format, and Label a Funnel Chart 136 Create a Pie Chart or Donut Chart 140 Format a Pie Chart or Donut Chart 142 Create a Treemap Chart 144 Format a Treemap Chart 146 Chapter 7 Showing Geographic Data on Maps Create a Proportional Symbol Map 150 Create a Choropleth Map 152 Add Conditional Formatting to a Choropleth Map 154 Enable Power BI’s Preview Features 156 Create an Isarithmic Map 158 Create a Skyscraper Map 160 Chapter 8 Using Calculated Columns and DAX Understanding DAX and Why You Should Use It 164 Add All Numbers in a Column 166 Perform Division 168 Check a Condition 170 Count the Number of Cells in a Column 172 Return the Average of All Numbers in a Column 174 Join Two Text Strings into One Text String 176 Apply Conditional Formatting in Tables 178 Chapter 9 Using Analytics and Machine Learning Identify Outliers 184 Find Groups of Similar Data by Clustering 186 Create a Dataflow 188 Apply Binary Prediction with AutoML 192 Chapter 10 Creating Interactive Reports Planning to Create a Report 198 Start a Report and Add a Title 200 Add Visuals to a Report 202 Add Slicers to a Report 206 Control Which Visuals and Slicers Interact 208 Enable and Control Drill-Through Actions 210 Split a Page into Sections 214 Add Bookmarks and Navigation to a Report 218 Chapter 11 Publishing Reports and Dashboards Set Up a Workspace 224 Ask Questions About the Data 226 Publish a Report to the Power BI Service 228 Set Up Row-Level Security 230 Add Tiles to a Dashboard 232 Share a Dashboard 234 Schedule Data Refreshes 236 Publish a Report to the Web 238 Index 240

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Book SynopsisTable of Contents1. Introduction 2. Data, measurements, and data processing 3. Data warehousing and online analytical processing 4. Pattern mining: basic concepts and methods 5. Pattern mining: advanced methods 6. Classification: basic concepts and methods 7. Classification: advanced methods 8. Cluster analysis: basic concepts and methods 9. Cluster analysis: advanced methods 10. Deep learning 11. Outlier Detection 12. Data mining trends and research frontiers Appendix: Mathematical background

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Book SynopsisAs AI permeates day-to-day lifeâinfluencing who is granted what type of healthcare, determining who is granted an employment interview or academic enrollment, or delivering what information to whomâit frames individuals and society. AI has far-reaching powers to improve lives, but it is not without risks. Due to its limitations, AI may prevent a person from receiving needed healthcare, block a deserving candidate from employment, or present fake and inaccurate information. How to trust that AI systems will improve lives and not create greater inequality and strife? How to foster the benefits of AI and mitigate the harms from AI? Ethical AI and Data Science: Building Trustworthy and Transparent Systems contributes answers to these questions.A vital and ongoing dialogue surrounding data, accessibility, and the multifaceted challenges posed by AI in ethics, safety, and societal impact, the book features diverse perspectives and practical approaches written by a panel of experts. Highlights of the book include: Ensuring safety in agentic AI through decision traceability Promoting responsible and ethical AI in financial services Bridging human-machine knowledge for responsible AI Responsible agentic AI for effective cybersecurity and ethical safety The book offers valuable insights into navigating the complexities of developing AI systems that are not only innovative but also transparent, equitable, and secure. This volume aims to advance understanding and foster collaboration among researchers, practitioners, and policymakers, helping to shape AI technologies that responsibly serve society while addressing critical concerns around fairness, accountability, and human well-being.

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Book SynopsisIn this book, authors Holden Karau and Boris Lublinsky show you how to scale existing Python applications and pipelines, allowing you to stay in the Python ecosystem while avoiding single points of failure and manual scheduling.

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Book SynopsisDetect 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.Table of ContentsList of Figures xv Foreword xxiii Preface xxv Acknowledgments xxix Chapter 1 Fraud: Detection, Prevention, and Analytics! 1 Introduction 2 Fraud! 2 Fraud Detection and Prevention 10 Big Data for Fraud Detection 15 Data-Driven Fraud Detection 17 Fraud-Detection Techniques 19 Fraud Cycle 22 The Fraud Analytics Process Model 26 Fraud Data Scientists 30 A Fraud Data Scientist Should Have Solid Quantitative Skills 30 A Fraud Data Scientist Should Be a Good Programmer 31 A Fraud Data Scientist Should Excel in Communication and Visualization Skills 31 A Fraud Data Scientist Should Have a Solid Business Understanding 32 A Fraud Data Scientist Should Be Creative 32 A Scientific Perspective on Fraud 33 References 35 Chapter 2 Data Collection, Sampling, and Preprocessing 37 Introduction 38 Types of Data Sources 38 Merging Data Sources 43 Sampling 45 Types of Data Elements 46 Visual Data Exploration and Exploratory Statistical Analysis 47 Benford’s Law 48 Descriptive Statistics 51 Missing Values 52 Outlier Detection and Treatment 53 Red Flags 57 Standardizing Data 59 Categorization 60 Weights of Evidence Coding 63 Variable Selection 65 Principal Components Analysis 68 RIDITs 72 PRIDIT Analysis 73 Segmentation 74 References 75 Chapter 3 Descriptive Analytics for Fraud Detection 77 Introduction 78 Graphical Outlier Detection Procedures 79 Statistical Outlier Detection Procedures 83 Break-Point Analysis 84 Peer-Group Analysis 85 Association Rule Analysis 87 Clustering 89 Introduction 89 Distance Metrics 90 Hierarchical Clustering 94 Example of Hierarchical Clustering Procedures 97 k-Means Clustering 104 Self-Organizing Maps 109 Clustering with Constraints 111 Evaluating and Interpreting Clustering Solutions 114 One-Class SVMs 117 References 118 Chapter 4 Predictive Analytics for Fraud Detection 121 Introduction 122 Target Definition 123 Linear Regression 125 Logistic Regression 127 Basic Concepts 127 Logistic Regression Properties 129 Building a Logistic Regression Scorecard 131 Variable Selection for Linear and Logistic Regression 133 Decision Trees 136 Basic Concepts 136 Splitting Decision 137 Stopping Decision 140 Decision Tree Properties 141 Regression Trees 142 Using Decision Trees in Fraud Analytics 143 Neural Networks 144 Basic Concepts 144 Weight Learning 147 Opening the Neural Network Black Box 150 Support Vector Machines 155 Linear Programming 155 The Linear Separable Case 156 The Linear Nonseparable Case 159 The Nonlinear SVM Classifier 160 SVMs for Regression 161 Opening the SVM Black Box 163 Ensemble Methods 164 Bagging 164 Boosting 165 Random Forests 166 Evaluating Ensemble Methods 167 Multiclass Classification Techniques 168 Multiclass Logistic Regression 168 Multiclass Decision Trees 170 Multiclass Neural Networks 170 Multiclass Support Vector Machines 171 Evaluating Predictive Models 172 Splitting Up the Data Set 172 Performance Measures for Classification Models 176 Performance Measures for Regression Models 185 Other Performance Measures for Predictive Analytical Models 188 Developing Predictive Models for Skewed Data Sets 189 Varying the Sample Window 190 Undersampling and Oversampling 190 Synthetic Minority Oversampling Technique (SMOTE) 192 Likelihood Approach 194 Adjusting Posterior Probabilities 197 Cost-sensitive Learning 198 Fraud Performance Benchmarks 200 References 201 Chapter 5 Social Network Analysis for Fraud Detection 207 Networks: Form, Components, Characteristics, and Their Applications 209 Social Networks 211 Network Components 214 Network Representation 219 Is Fraud a Social Phenomenon? An Introduction to Homophily 222 Impact of the Neighborhood: Metrics 227 Neighborhood Metrics 228 Centrality Metrics 238 Collective Inference Algorithms 246 Featurization: Summary Overview 254 Community Mining: Finding Groups of Fraudsters 254 Extending the Graph: Toward a Bipartite Representation 266 Multipartite Graphs 269 Case Study: Gotcha! 270 References 277 Chapter 6 Fraud Analytics: Post-Processing 279 Introduction 280 The Analytical Fraud Model Life Cycle 280 Model Representation 281 Traffic Light Indicator Approach 282 Decision Tables 283 Selecting the Sample to Investigate 286 Fraud Alert and Case Management 290 Visual Analytics 296 Backtesting Analytical Fraud Models 302 Introduction 302 Backtesting Data Stability 302 Backtesting Model Stability 305 Backtesting Model Calibration 308 Model Design and Documentation 311 References 312 Chapter 7 Fraud Analytics: A Broader Perspective 313 Introduction 314 Data Quality 314 Data-Quality Issues 314 Data-Quality Programs and Management 315 Privacy 317 The RACI Matrix 318 Accessing Internal Data 319 Label-Based Access Control (LBAC) 324 Accessing External Data 325 Capital Calculation for Fraud Loss 326 Expected and Unexpected Losses 327 Aggregate Loss Distribution 329 Capital Calculation for Fraud Loss Using Monte Carlo Simulation 331 An Economic Perspective on Fraud Analytics 334 Total Cost of Ownership 334 Return on Investment 335 In Versus Outsourcing 337 Modeling Extensions 338 Forecasting 338 Text Analytics 340 The Internet of Things 342 Corporate Fraud Governance 344 References 346 About the Authors 347 Index 349

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Book SynopsisTable of ContentsAcknowledgments xiii Foreword xxiii Introduction xxvii Part One Thinking Like a Data Head Chapter 1 What Is the Problem? 3 Questions a Data Head Should Ask 4 Why Is This Problem Important? 4 Who Does This Problem Affect? 6 What If We Don’t Have the Right Data? 6 When Is the Project Over? 7 What If We Don’t Like the Results? 7 Understanding Why Data Projects Fail 8 Customer Perception 8 Discussion 10 Working on Problems That Matter 11 Chapter Summary 11 Chapter 2 What Is Data? 13 Data vs. Information 13 An Example Dataset 14 Data Types 15 How Data Is Collected and Structured 16 Observational vs. Experimental Data 16 Structured vs. Unstructured Data 17 Basic Summary Statistics 18 Chapter Summary 19 Chapter 3 Prepare to Think Statistically 21 Ask Questions 22 There Is Variation in All Things 23 Scenario: Customer Perception (The Sequel) 24 Case Study: Kidney-Cancer Rates 26 Probabilities and Statistics 28 Probability vs. Intuition 29 Discovery with Statistics 31 Chapter Summary 33 Part Two Speaking Like a Data Head Chapter 4 Argue with the Data 37 What Would You Do? 38 Missing Data Disaster 39 Tell Me the Data Origin Story 43 Who Collected the Data? 44 How Was the Data Collected? 44 Is the Data Representative? 45 Is There Sampling Bias? 46 What Did You Do with Outliers? 46 What Data Am I Not Seeing? 47 How Did You Deal with Missing Values? 47 Can the Data Measure What You Want It to Measure? 48 Argue with Data of All Sizes 48 Chapter Summary 49 Chapter 5 Explore the Data 51 Exploratory Data Analysis and You 52 Embracing the Exploratory Mindset 52 Questions to Guide You 53 The Setup 53 Can the Data Answer the Question? 54 Set Expectations and Use Common Sense 54 Do the Values Make Intuitive Sense? 54 Watch Out: Outliers and Missing Values 58 Did You Discover Any Relationships? 59 Understanding Correlation 59 Watch Out: Misinterpreting Correlation 60 Watch Out: Correlation Does Not Imply Causation 62 Did You Find New Opportunities in the Data? 63 Chapter Summary 63 Chapter 6 Examine the Probabilities 65 Take a Guess 66 The Rules of the Game 66 Notation 67 Conditional Probability and Independent Events 69 The Probability of Multiple Events 69 Two Things That Happen Together 69 One Thing or the Other 70 Probability Thought Exercise 72 Next Steps 73 Be Careful Assuming Independence 74 Don’t Fall for the Gambler’s Fallacy 74 All Probabilities Are Conditional 75 Don’t Swap Dependencies 76 Bayes’ Theorem 76 Ensure the Probabilities Have Meaning 79 Calibration 80 Rare Events Can, and Do, Happen 80 Chapter Summary 81 Chapter 7 Challenge the Statistics 83 Quick Lessons on Inference 83 Give Yourself Some Wiggle Room 84 More Data, More Evidence 84 Challenge the Status Quo 85 Evidence to the Contrary 86 Balance Decision Errors 88 The Process of Statistical Inference 89 The Questions You Should Ask to Challenge the Statistics 90 What Is the Context for These Statistics? 90 What Is the Sample Size? 91 What Are You Testing? 92 What Is the Null Hypothesis? 92 Assuming Equivalence 93 What Is the Significance Level? 93 How Many Tests Are You Doing? 94 Can I See the Confidence Intervals? 95 Is This Practically Significant? 96 Are You Assuming Causality? 96 Chapter Summary 97 Part Three Understanding the Data Scientist’s Toolbox Chapter 8 Search for Hidden Groups 101 Unsupervised Learning 102 Dimensionality Reduction 102 Creating Composite Features 103 Principal Component Analysis 105 Principal Components in Athletic Ability 105 PCA Summary 108 Potential Traps 109 Clustering 110 k-Means Clustering 111 Clustering Retail Locations 111 Potential Traps 113 Chapter Summary 114 Chapter 9 Understand the Regression Model 117 Supervised Learning 117 Linear Regression: What It Does 119 Least Squares Regression: Not Just a Clever Name 120 Linear Regression: What It Gives You 123 Extending to Many Features 124 Linear Regression: What Confusion It Causes 125 Omitted Variables 125 Multicollinearity 126 Data Leakage 127 Extrapolation Failures 128 Many Relationships Aren’t Linear 128 Are You Explaining or Predicting? 128 Regression Performance 130 Other Regression Models 131 Chapter Summary 131 Chapter 10 Understand the Classification Model 133 Introduction to Classification 133 What You’ll Learn 134 Classification Problem Setup 135 Logistic Regression 135 Logistic Regression: So What? 138 Decision Trees 139 Ensemble Methods 142 Random Forests 143 Gradient Boosted Trees 143 Interpretability of Ensemble Models 145 Watch Out for Pitfalls 145 Misapplication of the Problem 146 Data Leakage 146 Not Splitting Your Data 146 Choosing the Right Decision Threshold 147 Misunderstanding Accuracy 147 Confusion Matrices 148 Chapter Summary 150 Chapter 11 Understand Text Analytics 151 Expectations of Text Analytics 151 How Text Becomes Numbers 153 A Big Bag of Words 153 N-Grams 157 Word Embeddings 158 Topic Modeling 160 Text Classification 163 Naïve Bayes 164 Sentiment Analysis 166 Practical Considerations When Working with Text 167 Big Tech Has the Upper Hand 168 Chapter Summary 169 Chapter 12 Conceptualize Deep Learning 171 Neural Networks 172 How Are Neural Networks Like the Brain? 172 A Simple Neural Network 173 How a Neural Network Learns 174 A Slightly More Complex Neural Network 175 Applications of Deep Learning 178 The Benefits of Deep Learning 179 How Computers “See” Images 180 Convolutional Neural Networks 182 Deep Learning on Language and Sequences 183 Deep Learning in Practice 185 Do You Have Data? 185 Is Your Data Structured? 186 What Will the Network Look Like? 186 Artificial Intelligence and You 187 Big Tech Has the Upper Hand 188 Ethics in Deep Learning 189 Chapter Summary 190 Part Four Ensuring Success Chapter 13 Watch Out for Pitfalls 193 Biases and Weird Phenomena in Data 194 Survivorship Bias 194 Regression to the Mean 195 Simpson’s Paradox 195 Confirmation Bias 197 Effort Bias (aka the “Sunk Cost Fallacy”) 197 Algorithmic Bias 198 Uncategorized Bias 198 The Big List of Pitfalls 199 Statistical and Machine Learning Pitfalls 199 Project Pitfalls 200 Chapter Summary 202 Chapter 14 Know the People and Personalities 203 Seven Scenes of Communication Breakdowns 204 The Postmortem 204 Storytime 205 The Telephone Game 206 Into the Weeds 206 The Reality Check 207 The Takeover 207 The Blowhard 208 Data Personalities 208 Data Enthusiasts 209 Data Cynics 209 Data Heads 209 Chapter Summary 210 Chapter 15 What’s Next? 211 Index 215

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Book SynopsisTwo 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.Tech 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.In Fusion Strategy, 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.The laws of

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Book SynopsisWhether you already work with data or want to learn more about its possibilities, the examples and techniques in this practical book will help you more easily clean, evaluate, and analyze data so that you can generate meaningful insights and compelling visualizations.

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Book SynopsisSnowflake'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.

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Book SynopsisDo your product dashboards look funky? Are your quarterly reports stale? Is the dataset you're using broken or just plain wrong? These problems affect almost every team, yet they're usually addressed on an ad hoc basis and in a reactive manner. If you answered yes to any of the questions above, this book is for you.

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Book SynopsisManage 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.

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Book SynopsisIn 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.

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Book SynopsisAlthough change is constant in business and analytics, some fundamental principles and lessons learned are truly timeless, extending and surviving beyond the rapid ongoing evolution of tools, techniques, and technologies. Through a series of articles published over the course of his 30+ year career in analytics and technology, author Doug Gray shares the most important lessons he has learned â with colleagues and students as well â that have helped to ensure success on his journey as a practitioner, leader, and educator.The reader witnesses the Analytical Sciences profession through the mindâs eye of a practitioner who has operated at the forefront of analytically-inclined organizations, such as American Airlines and Walmart, delivering solutions that generate hundreds of millions of dollars annually in business value, and an educator teaching students and conducting research at a leading university. Through real-world project case studies, first-hand stories, and practical e

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Book SynopsisThis practical guide shows postdoc bioinformatics professionals and students how to exploit the best parts of Python to solve problems in biology while creating documented, tested, reproducible software.

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Book SynopsisIf you are a biologist and want to get the best out of the powerful methods of modern computational statistics, this is your book. You can visualize and analyze your own data, apply unsupervised and supervised learning, integrate datasets, apply hypothesis testing, and make publication-quality figures using the power of R/Bioconductor and ggplot2. This book will teach you ''cooking from scratch'', from raw data to beautiful illuminating output, as you learn to write your own scripts in the R language and to use advanced statistics packages from CRAN and Bioconductor. It covers a broad range of basic and advanced topics important in the analysis of high-throughput biological data, including principal component analysis and multidimensional scaling, clustering, multiple testing, unsupervised and supervised learning, resampling, the pitfalls of experimental design, and power simulations using Monte Carlo, and it even reaches networks, trees, spatial statistics, image data, and microbial ecology. Using a minimum of mathematical notation, it builds understanding from well-chosen examples, simulation, visualization, and above all hands-on interaction with data and code.Trade Review'This is a gorgeous book, both visually and intellectually, superbly suited for anyone who wants to learn the nuts and bolts of modern computational biology. It can also be a practical, hands-on starting point for life scientists and students who want to break out of 'canned packages' into the more versatile world of R coding. Much richer than the typical statistics textbook, it covers a wide range of topics in machine learning and image processing. The chapter on making high-quality graphics is alone worth the price of the book.' William H. Press, University of Texas, Austin'The book is a timely, comprehensive and practical reference for anyone working with modern quantitative biotechnologies. It can be read at multiple levels. For scientists with a statistics background, it is a thorough review of key methods for design and analysis of high-throughput experiments. For life scientists with a limited exposure to statistics, it offers a series of examples with relevant data and R code. Avoiding buzzwords and hype, the book advocates appropriate statistical practice for reproducible research. I expect it to be as influential for the life sciences community as Modern Applied Statistics with S, by Venables and Ripley or Introduction to Statistical Learning, by James, Witten, Hastie and Tibshirani are for applied statistics.' Olga Vitek, Northeastern University, Boston'Navigating rich data to arrive at sensible insight requires confidence in our biological understanding, informatic ability, statistical sophistication, and skills at effective communication. Fortunately the wisdom and effort of the worldwide research community has been distilled into accessible and rich collections of R and Bioconductor software packages. Holmes and Huber provide a comprehensive guide to navigating modern statistical methods for working with complex, large, and nuanced biological data. The presentation provides a firm conceptual foundation coupled with worked practical examples, extended analysis, and refined discussion of practical and theoretical challenges facing the modern practitioner. This book provides us with the confidence and tools necessary for the analysis and comprehension of modern biological data using modern statistical methods.' Martin Morgan, Roswell Park Comprehensive Cancer Center, leader of the Bioconductor project'Holmes and Huber take an integrated approach to presenting the key statistical concepts and methods needed for the analysis of biological data. Specifically, they do a wonderful job of building these foundations in the context of modern computational tools, genuine scientific questions, and real-world datasets. The code showcases many of the newest features of R and its dynamic package ecosystem, such as using ggplot2 for visualization and dplyr for data manipulation.' Jenny Bryan, RStudio and University of British Columbia'... the book is extremely readable and engaging, it explains complicated concepts in simple terms, and uses illuminating graphics and examples. Any researcher who wants to learn or teach up-to-date statistics to biologists will find this an essential volume for modern teaching of modern statistics to modern biologists.' Noa Pinter-Wollman, The Quarterly Review of BiologyTable of ContentsIntroduction; 1. Generative models for discrete data; 2. Statistical modeling; 3. High-quality graphics in R; 4. Mixture models; 5. Clustering; 6. Testing; 7. Multivariate analysis; 8. High-throughput count data; 9. Multivariate methods for heterogeneous data; 10. Networks and trees; 11. Image data; 12. Supervised learning; 13. Design of high-throughput experiments and their analyses; Statistical concordance; Bibliography; Index.

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Book SynopsisGetting 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.Trade Review'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'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'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'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'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 & Chairman of the Executive Committee'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'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'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 & Analytics'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'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'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'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'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'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'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 MachineTable of ContentsPreface – 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.

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Book SynopsisDiscover how to achieve business goals by relying on high-quality, robust data In Data Quality: Empowering Businesses with Analytics and AI, 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. The author shows you how to: Profile for data quality, including the appropriate techniques, criteria, and KPIs Identify 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. Formulate the reference architecture for data quality, inTable of ContentsForeword by Bill Inmon Preface About the Book Quality Principles Applied in This Book Organization of the Book Who Should Read This Book? References Acknowledgments Define Phase Chapter 1: Introduction Introduction Data, Analytics, AI, and Business Performance Data as a Business Asset or Liability Data Governance, Data Management, and Data Quality Leadership Commitment to Data Quality Key Takeaways Conclusion References Chapter 2: Business Data Introduction Data in Business Telemetry Data Purpose of Data in Business Business Data Views Key Characteristics of Business Data Critical Data Elements (CDE) Key Takeaways Conclusion References Chapter 3: Data Quality in Business Introduction Data Quality Dimensions Context in Data Quality Consequences and Costs of Poor Data Quality Data Depreciation and Its Factors Data in IT Systems Data Quality and Trusted Information Key Takeaways Conclusion References Analyze Phase Chapter 4: Causes for Poor Data Quality Introduction Data Quality RCA Techniques Typical Causes of Poor Data Quality Key Takeaways Conclusion References Chapter 5: Data Lifecycle and Lineage Introduction Business-Enabled DLC Stages IT Business-Enabled DLC Stages Data Lineage Key Takeaways Conclusion References Chapter 6: Profiling for Data Quality Introduction Criteria for Data Profiling Data Profiling Techniques for Measures of Centrality Data Profiling Techniques for Measures of Variation Integrating Centrality and Variation KPIs Key Takeaways Conclusion References Realize Phase Chapter 7: Reference Architecture for Data Quality Introduction Options to Remediate Data Quality DataOps Data Product Data Fabric and Data Mesh Data Enrichment Key Takeaways Conclusion References Chapter 8: Best Practices to Realize Data Quality Introduction Overview of Best Practices BP 1: Identify the Business KPIs and the Ownership of These KPIs and the Pertinent Data BP 2: Build and Improve the Data Culture and Literacy in the Organization BP 3: Define the Current and Desired state of Data Quality BP 4: Follow the Minimalistic Approach to Data Capture BP 5: Select and Define the Data Attributes for Data Quality BP 6: Capture and Manage Critical Data with Data Standards in MDM Systems Key Takeaways Conclusion References Chapter 9: Best Practices to Realize Data Quality Introduction BP 7: Automate the Integration of Critical Data Elements BP 8: Define the SoR and Securely Capture Transactional Data in the SoR/OLTP System BP 9: Build and Manage Robust Data Integration Capabilities BP 10: Distribute Data Sourcing and Insight Consumption Key Takeaways Conclusion References Sustain Phase Chapter 10: Data Governance Introduction Data Governance Principles Data Governance Design Components Implementing the Data Governance Program Data Observability Data Compliance – ISO 27001 and SOC2 Key Takeaways Conclusion References Chapter 11: Protecting Data Introduction Data Classification Data Safety Data Security Key Takeaways Conclusion References Chapter 12: Data Ethics Introduction Data Ethics Importance of Data Ethics Principles of Data Ethics Model Drift in Data Ethics Data Privacy Managing Data Ethically Key Takeaways Conclusion References Appendix 1: Abbreviations and Acronyms Appendix 2: Glossary Appendix 3: Data Literacy Competencies About the Author Index

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Book SynopsisWith 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.

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Book SynopsisDevelop 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. Beginning Apache Spark 2 gives you an introduction to Apache Spark and shows you how to work with it.Along 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.  After 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.  What You Will Learn   Understand Spark unified data processing platform How

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Book SynopsisArtificial intelligence touches nearly every part of your day. While you may initially assume that technology such as smart speakers and digital assistants are the extent of it, AI has in fact rapidly become a general-purpose technology, reverberating across industries including transportation, healthcare, financial services, and many more. In our modern era, an understanding of AI and its possibilities for your organization is essential for growth and success.Artificial Intelligence Basics has arrived to equip you with a fundamental, timely grasp of AI and its impact. Author Tom Taulli provides an engaging, non-technical introduction to important concepts such as machine learning, deep learning, natural language processing (NLP), robotics, and more. In addition to guiding you through real-world case studies and practical implementation steps, Taulli uses his expertise to expand on the bigger questions that surround AI. These include societal trends, ethics, andTable of Contents

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Book SynopsisBuilding 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 yoTable of ContentsFOREWORD – 1 page ABOUT THE AUTHORS – 1 page ACKNOWLEDGMENTS – 1 page INTRODUCTION – 2 pages What is this book about? Who should read this book? Why did the authors write this book? Organization of the book CHAPTERS Chapter 1—Business Case (~15 pages) This 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. Chapter 2—IT Security Mind Set (~15 pages) This chapter presents thinking like an IT security professional to establish and maintain common security protections. Chapter 3—IT Security Risk Management (~15 pages) This chapter presents a risk management process that involves asset management, security vulnerabilities, security threats, risk identification, risk mitigation, and security controls. Chapter 4—IT Security Process (~15 pages) This chapter presents how to establish security scopes and select corresponding controls to protect the confidentiality, availability, and integrity of company information. Chapter 5—IT Security Scenarios and Perspectives (~40 pages) This 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. IT security at home IT security while traveling IT security at work IT security as an executive International IT security Chapter 6—Planning IT Security Awareness and Training (~15 pages) This chapter presents practical guidance on how to write an IT Awareness and Training implementation plan. Chapter 7—Implementing IT Security Awareness and Training Program(~15 pages) This chapter presents human issues related to bringing about enterprise-wide cultural change due to implementation of an IT Awareness and Training Program. Chapter 8—Measuring IT Security Awareness and Training Program Implementation (~15 pages) This chapter presents practical guidance for measuring program implementation success and how to use the measurements to achieve awareness and training goals. Chapter 9—Managing Continual Program Improvement (~15 pages) This chapter presents practical guidance for monitoring compliance, evaluating feedback and improving the program. Chapter 10—Looking to the Future (~15 pages) This chapter presents a view of the evolving cybersecurity attacks as they become more capable and sophisticated. APPENDICES – 10 pages GLOSSARY – 3 pages BIBLIOGRAPHY – 3 pages INDEX – 4 pages

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Book SynopsisWith 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. Marco 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. Tabular Modeling in Microsoft SQL Server Analysis Services will 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 Microsoft SQL Server 2012 Analysis Services: The BISM Tabular Model.Table of Contents CHAPTER 1 Introducing the tabular model CHAPTER 2 Getting started with the tabular model CHAPTER 3 Loading data inside Tabular CHAPTER 4 Introducing calculations in DAX CHAPTER 5 Building hierarchies CHAPTER 6 Data modeling in Tabular CHAPTER 7 Tabular Model Scripting Language (TMSL) CHAPTER 8 The tabular presentation layer CHAPTER 9 Using DirectQuery CHAPTER 10 Security CHAPTER 11 Processing and partitioning tabular models CHAPTER 12 Inside VertiPaq CHAPTER 13 Interfacing with Tabular CHAPTER 14 Monitoring and tuning a Tabular service CHAPTER 15 Optimizing tabular models CHAPTER 16 Choosing hardware and virtualization

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Book SynopsisChapters 6 and 7 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 8, 9, and 10 discuss recurrent neural networks, convolutional neural networks, and graph neural networks.

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Book SynopsisThe immense increase on the size and type of real time data generated across various edge computing platform results in unstructured databases and data silos. This edited book gathers together an international set of researchers to investigate the possibilities offered by data-fabric solutions; the volume focuses in particular on data architectures and on semantic changes in future data landscapes.

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Book SynopsisThe development of information technology has made it possible to collect large amounts of spatial data on a daily basis. It is of enormous significance when it comes to discovering implicit, non-trivial and potentially valuable information from this spatial data. Spatial co-location patterns reveal the distribution rules of spatial features, which can be valuable for application users. This book provides commercial software developers with proven and effective algorithms for detecting and filtering these implicit patterns, and includes easily implemented pseudocode for all the algorithms. Furthermore, it offers a basis for further research in this promising field.Preference-based co-location pattern mining refers to mining constrained or condensed co-location patterns instead of mining all prevalent co-location patterns. Based on the authors’ recent research, the book highlights techniques for solving a range of problems in this context, including maximal co-location pattern mining, closed co-location pattern mining, top-k co-location pattern mining, non-redundant co-location pattern mining, dominant co-location pattern mining, high utility co-location pattern mining, user-preferred co-location pattern mining, and similarity measures between spatial co-location patterns.Presenting a systematic, mathematical study of preference-based spatial co-location pattern mining, this book can be used both as a textbook for those new to the topic and as a reference resource for experienced professionals.Table of Contents

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Book SynopsisUpdated 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.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisDaniel Chen is a graduate student in the Interdisciplinary PhD program in Genetics, Bioinformatics & 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.Table of ContentsForeword by Anne M. Brown xxiii Foreword by Jared Lander xxv Preface xxvii Changes in the Second Edition xxxix Part I: Introduction 1 Chapter 1. Pandas DataFrame Basics 3 Learning Objectives 3 1.1 Introduction 3 1.2 Load Your First Data Set 4 1.3 Look at Columns, Rows, and Cells 6 1.4 Grouped and Aggregated Calculations 23 1.5 Basic Plot 27 Conclusion 28 Chapter 2. Pandas Data Structures Basics 31 Learning Objectives 31 2.1 Create Your Own Data 31 2.2 The Series 33 2.3 The DataFrame 42 2.4 Making Changes to Series and DataFrames 45 2.5 Exporting and Importing Data 52 Conclusion 63 Chapter 3. Plotting Basics 65 Learning Objectives 65 3.1 Why Visualize Data? 65 3.2 Matplotlib Basics 66 3.3 Statistical Graphics Using matplotlib 72 3.4 Seaborn 78 3.5 Pandas Plotting Method 111 Conclusion 115 Chapter 4. Tidy Data 117 Learning Objectives 117 Note About This Chapter 117 4.1 Columns Contain Values, Not Variables 118 4.2 Columns Contain Multiple Variables 122 4.3 Variables in Both Rows and Columns 126 Conclusion 129 Chapter 5. Apply Functions 131 Learning Objectives 131 Note About This Chapter 131 5.1 Primer on Functions 131 5.2 Apply (Basics) 133 5.3 Vectorized Functions 138 5.4 Lambda Functions (Anonymous Functions) 141 Conclusion 142 Part II: Data Processing 143 Chapter 6. Data Assembly 145 Learning Objectives 145 6.1 Combine Data Sets 145 6.2 Concatenation 146 6.3 Observational Units Across Multiple Tables 154 6.4 Merge Multiple Data Sets 160 Conclusion 167 Chapter 7. Data Normalization 169 Learning Objectives 169 7.1 Multiple Observational Units in a Table (Normalization) 169 Conclusion 173 Chapter 8. Groupby Operations: Split-Apply-Combine 175 Learning Objectives 175 8.1 Aggregate 176 8.2 Transform 184 8.3 Filter 188 8.4 The pandas.core.groupby.DataFrameGroupBy object 190 8.5 Working with a MultiIndex 195 Conclusion 199 Part III: Data Types 203 Chapter 9. Missing Data 203 Learning Objectives 203 9.1 What Is a NaN Value? 203 9.2 Where Do Missing Values Come From? 205 9.3 Working with Missing Data 210 9.4 Pandas Built-In NA Missing 216 Conclusion 218 Chapter 10. Data Types 219 Learning Objectives 219 10.1 Data Types 219 10.2 Converting Types 220 10.3 Categorical Data 225 Conclusion 227 Chapter 11. Strings and Text Data 229 Introduction 229 Learning Objectives 229 11.1 Strings 229 11.2 String Methods 233 11.3 More String Methods 234 11.4 String Formatting (F-Strings) 236 11.5 Regular Expressions (RegEx) 239 11.6 The regex Library 247 Conclusion 247 Chapter 12. Dates and Times 249 Learning Objectives 249 12.1 Python's datetime Object 249 12.2 Converting to datetime 250 12.3 Loading Data That Include Dates 253 12.4 Extracting Date Components 254 12.5 Date Calculations and Timedeltas 257 12.6 Datetime Methods 259 12.7 Getting Stock Data 261 12.8 Subsetting Data Based on Dates 263 12.9 Date Ranges 266 12.10 Shifting Values 270 12.11 Resampling 276 12.12 Time Zones 278 12.13 Arrow for Better Dates and Times 280 Conclusion 280 Part IV: Data Modeling 281 Chapter 13. Linear Regression (Continuous Outcome Variable) 283 13.1 Simple Linear Regression 283 13.2 Multiple Regression 287 13.3 Models with Categorical Variables 289 13.4 One-Hot Encoding in scikit-learn with Transformer Pipelines 294 Conclusion 296 Chapter 14. Generalized Linear Models 297 About This Chapter 297 14.1 Logistic Regression (Binary Outcome Variable) 297 14.2 Poisson Regression (Count Outcome Variable) 304 14.3 More Generalized Linear Models 308 Conclusion 309 Chapter 15. Survival Analysis 311 15.1 Survival Data 311 15.2 Kaplan Meier Curves 312 15.3 Cox Proportional Hazard Model 314 Conclusion 317 Chapter 16. Model Diagnostics 319 16.1 Residuals 319 16.2 Comparing Multiple Models 324 16.3 k-Fold Cross-Validation 329 Conclusion 334 Chapter 17. Regularization 335 17.1 Why Regularize? 335 17.2 LASSO Regression 337 17.3 Ridge Regression 338 17.4 Elastic Net 340 17.5 Cross-Validation 341 Conclusion 343 Chapter 18. Clustering 345 18.1 k-Means 345 18.2 Hierarchical Clustering 351 Conclusion 356 Part V. Conclusion 357 Chapter 19. Life Outside of Pandas 359 19.1 The (Scientific) Computing Stack 359 19.2 Performance 360 19.3 Dask 360 19.4 Siuba 360 19.5 Ibis 361 19.6 Polars 361 19.7 PyJanitor 361 19.8 Pandera 361 19.9 Machine Learning 361 19.10 Publishing 362 19.11 Dashboards 362 Conclusion 362 Chapter 20. It's Dangerous To Go Alone! 363 20.1 Local Meetups 363 20.2 Conferences 363 20.3 The Carpentries 364 20.4 Podcasts 364 20.5 Other Resources 365 Conclusion 365 Appendices 367 A. Concept Maps 369B. Installation and Setup 373C. Command Line 377D. Project Templates 379E. Using Python 381F. Working Directories 383G. Environments 385H. Install Packages 389I. Importing Libraries 391J. Code Style 393K. Containers: Lists, Tuples, and Dictionaries 395L. Slice Values 399M. Loops 401N. Comprehensions 403O. Functions 405P. Ranges and Generators 409Q. Multiple Assignment 413R. Numpy ndarray 415S. Classes 417T. SettingWithCopyWarning 419U. Method Chaining 423V. Timing Code 427W. String Formatting 429X. Conditionals (if-elif-else) 433Y. New York ACS Logistic Regression Example 435Z. Replicating Results in R 443 Index 451

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Book SynopsisAs 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.

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Book SynopsisMaster the demand forecasting skills you need to decide what resources to acquire, products to produce, and where and how to distribute them. For demand planners, S&OP managers, supply chain leaders, and data scientists. Demand Forecasting Best Practices is a unique step-by-step guide, demonstrating forecasting tools, metrics, and models alongside stakeholder management techniques that work in a live business environment. You will learn how to: Lead a demand planning team to improve forecasting quality while reducing workload Properly define the objectives, granularity, and horizon of your demand planning process Use smart, value-weighted KPIs to track accuracy and bias Spot areas of your process where there is room for improvement Help planners and stakeholders (sales, marketing, finances) add value to your process Identify what kind of data you should be collecting, and how Utilise different types of statistical and machine learning models Follow 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. About the technology Demand 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.

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Book SynopsisTest-driven data analysis is the synthesis of ideas from test-driven development of software to data-intensive work including data science, data analysis, and data engineering. It is a methodology for improving the quality of data and of analytical pipelines and processes. It can be thought of as data analysis as if the answers actually matter.Part I of the book describes the use of constraints for data validation and shows how tools can use a machine-learning-style approach to infer suitable constraints from data. This is illustrated using the command-line tools in the open-source Python tdda library, as well as alternatives. These can be used regardless of the programming language in use. Part II covers reference testing of analytical pipelines and processes. Again, there is support in the tdda library for this, including automatic test-generation capabilities for testing software in any language. There are specific discussions of testing models and modeling, including the large language models used in chatbots, coding bots, and summarizers. Part III covers broader kinds of errors that can occur in analysis, including errors of formulation, when the problem domain, methods or libraries are misinterpreted; errors of process, as developed analytical processes are used; errors of applicability, when analytical processes are used in situations for which they were not designed; errors of communication, when valid analyses are misinterpreted; and errors of judgement, when harmful analyses are carried out. Extensive checklists are provided that can be used to improve quality before, during, and after analysis.Test-driven data analysis can be thought of as a sibling to reproducible research, with similar concerns, but greater emphasis on automated testing, and less requirement for a human to reproduce results.Key Features: Prevents costly errors in analytical processes before they reach production through automated data validation and reference testing of data pipelines. Provides actionable checklists for issues beyond the reach of automated testing. Equips readers with open-source Python tools and language-agnostic command-line interfaces. Addresses testing challenges for modern LLM-based systems including chatbots and coding assistants. Instills in analysts an inner voice that is always asking: âœHow is this misleading data misleading me?â Nicholas Radcliffe is the Founder and Director of Stochastic Solutions Limited, a Scottish company specializing in consulting in data science, data analysis, and data engineering. He has also, since 1995, been a Visiting Professor in the Operations Research Group in the School of Mathematics at the University of Edinburgh. He is known for developing forma analysis (sic) of genetic algorithms and uplift modeling, before more recent work on test-driven data analysis.

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Book SynopsisIn the second edition of this practical guide, you'll learn how to conduct analytics on data where it lives, whether it's a data lake using Hive, a modern lakehouse with Iceberg or Delta Lake, a different system like Cassandra, Kafka, or SingleStore, or a relational database like PostgreSQL or Oracle.

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Book SynopsisA holistic and real-world approach to operationalizing artificial intelligence in your company InOperating AI, Director of Technology and Architecture at Ericsson AB, Ulrika Jägare, delivers an eye-opening new discussion of how to introduce your organization to artificial intelligence by balancing data engineering, model development, and AI operations. You'll learn the importance of embracing an AI operational mindset to successfully operate AI and lead AI initiatives through the entire lifecycle, including keyareas such as; data mesh, data fabric,aspects ofsecurity,data privacy,data rights and IPR related to data and AI models. In the book, you'll also discover: How to reduce the risk of entering bias in our artificial intelligence solutions and how to approach explainable AI (XAI)The importance of efficient and reproduceable data pipelines, including how to manage your company's dataAn operational perspective on the development of AI models using the MLOps (Machine Learning Operations) approach, including how to deploy, run and monitor models and ML pipelines in production using CI/CD/CT techniques, that generates value in the real worldKey competences and toolsets in AI development, deployment and operationsWhat to consider when operating different types of AI business models With a strong emphasis on deployment and operations of trustworthy and reliable AI solutions that operate well in the real worldand not just the labOperating AIis a must-read for business leaders looking for ways to operationalize an AI business model that actually makes money, from the concept phase to running in a live production environment.Table of ContentsForeword xii Introduction xv Chapter 1 Balancing the AI Investment 1 Defining AI and Related Concepts 3 Operational Readiness and Why It Matters 8 Applying an Operational Mind- set from the Start 12 The Operational Challenge 15 Strategy, People, and Technology Considerations 19 Strategic Success Factors in Operating AI 20 People and Mind- sets 23 The Technology Perspective 28 Chapter 2 Data Engineering Focused on AI 31 Know Your Data 32 Know the Data Structure 32 Know the Data Records 34 Know the Business Data Oddities 35 Know the Data Origin 36 Know the Data Collection Scope 37 The Data Pipeline 38 Types of Data Pipeline Solutions 41 Data Quality in Data Pipelines 44 The Data Quality Approach in AI/ML 45 Scaling Data for AI 49 Key Capabilities for Scaling Data 51 Introducing a Data Mesh 53 When You Have No Data 55 The Role of a Data Fabric 56 Why a Data Fabric Matters in AI/ML 58 Key Competences and Skillsets in Data Engineering 60 Chapter 3 Embracing MLOps 71 MLOps as a Concept 72 From ML Models to ML Pipelines 76 The ML Pipeline 78 Adopt a Continuous Learning Approach 84 The Maturity of Your AI/ML Capability 86 Level 0— Model Focus and No MLOps 88 Level 1— Pipelines Rather than Models 89 Level 2— Leveraging Continuous Learning 90 The Model Training Environment 91 Enabling ML Experimentation 92 Using a Simulator for Model Training 94 Environmental Impact of Training AI Models 96 Considering the AI/ML Functional Technology Stack 97 Key Competences and Toolsets in MLOps 103 Clarifying Similarities and Differences 106 MLOps Toolsets 107 Chapter 4 Deployment with AI Operations in Mind 115 Model Serving in Practice 117 Feature Stores 118 Deploying, Serving, and Inferencing Models at Scale 121 The ML Inference Pipeline 123 Model Serving Architecture Components 125 Considerations Regarding Toolsets for Model Serving 129 The Industrialization of AI 129 The Importance of a Cultural Shift 139 Chapter 5 Operating AI Is Different from Operating Software 143 Model Monitoring 144 Ensuring Efficient ML Model Monitoring 145 Model Scoring in Production 146 Retraining in Production Using Continuous Training 151 Data Aspects Related to Model Retraining 155 Understanding Different Retraining Techniques 156 Deployment after Retraining 159 Disadvantages of Retraining Models Frequently 159 Diagnosing and Managing Model Performance Issues in Operations 161 Issues with Data Processing 162 Issues with Data Schema Change 163 Data Loss at the Source 165 Models Are Broken Upstream 166 Monitoring Data Quality and Integrity 167 Monitoring the Model Calls 167 Monitoring the Data Schema 168 Detecting Any Missing Data 168 Validating the Feature Values 169 Monitor the Feature Processing 170 Model Monitoring for Stakeholders 171 Ensuring Stakeholder Collaboration for Model Success 173 Toolsets for Model Monitoring in Production 175 Chapter 6 AI Is All About Trust 181 Anonymizing Data 182 Data Anonymization Techniques 185 Pros and Cons of Data Anonymization 187 Explainable AI 189 Complex AI Models Are Harder to Understand 190 What Is Interpretability? 191 The Need for Interpretability in Different Phases 192 Reducing Bias in Practice 194 Rights to the Data and AI Models 199 Data Ownership 200 Who Owns What in a Trained AI Model? 202 Balancing the IP Approach for AI Models 205 The Role of AI Model Training 206 Addressing IP Ownership in AI Results 207 Legal Aspects of AI Techniques 208 Operational Governance of Data and AI 210 Chapter 7 Achieving Business Value from AI 215 The Challenge of Leveraging Value from AI 216 Productivity 216 Reliability 217 Risk 218 People 219 Top Management and AI Business Realization 219 Measuring AI Business Value 223 Measuring AI Value in Nonrevenue Terms 227 Operating Different AI Business Models 229 Operating Artificial Intelligence as a Service 230 Operating Embedded AI Solutions 236 Operating a Hybrid AI Business Model 239 Index 241

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Book Synopsis Human attention is in the highest demand it has ever been. The drastic increase in available information has compelled individuals to find a way to sift through the media that is literally at their fingertips. Content recommendation systems have emerged as the technological solution to this social and informational problem, but they''ve also created a bigger crisis in confirming our biases by showing us only, and exactly, what it predicts we want to see.   Data versus Democracy investigates and explores how, in the era of social media, human cognition, algorithmic recommendation systems, and human psychology are all working together to reinforce (and exaggerate) human bias. The dangerous confluence of these factors is driving media narratives, influencing opinions, and possibly changing election results.  In this book, algorithmic recommendations, clickbait, familiarity bias, propaganda, Trade Review“A very well written book that has an engaging style of writing, doesn’t become dry or bogged down in the details, but still showcases the depth of knowledge that Shaffer has on the subject. … It’s accessible and it provides a satisfying read to those looking for deep analysis of this emerging problem faced by the world.” (The Robotics Law Journal, Vol. 5 (2), September - October, 2019) Table of ContentsPart I: The Propaganda Problem.- Chapter 1: Pay Attention: How Information Abundance Affects the Way We Consume Media .- Chapter 2: Cog in the System: How the Limits of Our Brains Leave Us Vulnerable to Cognitive Hacking.- Chapter 3: Swimming Upstream: How Content Recommendation Engines Impact Information and Manipulate Our Attention.- Part II: Case Studies.- Chapter 4: Domestic Disturbance: Ferguson, GamerGate, and the Rise of the American Alt-Right.- Chapter 5: Democracy Hacked, Part 1: Russian Interference and the New Cold War .- Chapter 6: Democracy Hacked, Part 2: Rumors, Bots, and Genocide in the Global South .- Chapter 7: Conclusion: Where Do We Go from Here?.-

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