Data mining Books, bestsellers and new releases

Springer-Verlag New York Inc. The Elements of Statistical Learning Springer

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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£55.24

Taylor & Francis Ltd Actionable Intelligence in Healthcare

2 in stock

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

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John Wiley & Sons Inc Becoming a Data Head

2 in stock

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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£26.40

John Wiley & Sons Inc Teach Yourself Visually Power Bi

3 in stock

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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3 in stock

£19.54

O'Reilly Media Beautiful Visualization Looking At Data Through

1 in stock

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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O'Reilly Media Snowflake The Definitive Guide

5 in stock

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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5 in stock

£47.99

Elsevier Science & Technology Data Mining: Concepts and Techniques

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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Pearson Education (US) Learning Deep Learning

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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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£46.54

CRC Press The Art of Data Science

1 in stock

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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CRC Press Ensemble Methods

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Book SynopsisGraph Databases in Action teaches readers everything they need to know to begin building and running applications powered by graph databases. Right off the bat, seasoned graph database experts introduce readers to just enough graph theory, the graph database ecosystem, and a variety of datastores. They also explore modelling basics in action with real-world examples, then go hands-on with querying, coding traversals, parsing results, and other essential tasks as readers build their own graph-backed social network app complete with a recommendation engine! Key Features · Graph database fundamentals · An overview of the graph database ecosystem · Relational vs. graph database modelling · Querying graphs using Gremlin · Real-world common graph use cases For readers with basic Java and application development skills building in RDBMS systems such as Oracle, SQL Server, MySQL, and Postgres. No experience with graph databases is required. About the technology Graph databases store interconnected data in a more natural form, making them superior tools for representing data with rich relationships. Unlike in relational database management systems (RDBMS), where a more rigid view of data connections results in the loss of valuable insights, in graph databases, data connections are first priority. Dave Bechberger has extensive experience using graph databases as a product architect and a consultant. He’s spent his career leveraging cutting-edge technologies to build software in complex data domains such as bioinformatics, oil and gas, and supply chain management. He’s an active member of the graph community and has presented on a wide variety of graph-related topics at national and international conferences. Josh Perryman is technologist with over two decades of diverse experience building and maintaining complex systems, including high performance computing (HPC) environments. Since 2014 he has focused on graph databases, especially in distributed or big data environments, and he regularly blogs and speaks at conferences about graph databases.

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£37.99

Manning Publications Time Series Forecasting in Python

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 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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£24.99

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