Details

The Data Science Handbook

1. Aufl.

von: Field Cady
44,99 €
Verlag:	Wiley
Format:	PDF
Veröffentl.:	20.01.2017
ISBN/EAN:	9781119092933
Sprache:	englisch
Anzahl Seiten:	416

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Beschreibungen

Titelbeschreibung

A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master the discipline Finding a good data scientist has been likened to hunting for a unicorn: the required combination of technical skills is simply very hard to find in one person. In addition, good data science is not just rote application of trainable skill sets; it requires the ability to think flexibly about all these areas and understand the connections between them. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. Unlike many analytics books, computer science and software engineering are given extensive coverage since they play such a central role in the daily work of a data scientist. The author also describes classic machine learning algorithms, from their mathematical foundations to real-world applications. Visualization tools are reviewed, and their central importance in data science is highlighted. Classical statistics is addressed to help readers think critically about the interpretation of data and its common pitfalls. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter, and all topics are explained in the context of solving real-world data problems. The book also features: • Extensive sample code and tutorials using Python™ along with its technical libraries • Core technologies of “Big Data,” including their strengths and limitations and how they can be used to solve real-world problems • Coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presented, it is done in an intuitive way to encourage critical thinking and creativity • A wide variety of case studies from industry • Practical advice on the realities of being a data scientist today, including the overall workflow, where time is spent, the types of datasets worked on, and the skill sets needed The Data Science Handbook is an ideal resource for data analysis methodology and big data software tools. The book is appropriate for people who want to practice data science, but lack the required skill sets. This includes software professionals who need to better understand analytics and statisticians who need to understand software. Modern data science is a unified discipline, and it is presented as such. This book is also an appropriate reference for researchers and entry-level graduate students who need to learn real-world analytics and expand their skill set. FIELD CADY is the data scientist at the Allen Institute for Artificial Intelligence, where he develops tools that use machine learning to mine scientific literature. He has also worked at Google and several Big Data startups. He has a BS in physics and math from Stanford University, and an MS in computer science from Carnegie Mellon.

Inhaltsverzeichnis

Preface xvii 1 Introduction: Becoming a Unicorn 1 1.1 Aren’t Data Scientists Just Overpaid Statisticians? 2 1.2 How is This Book Organized? 3 1.3 How to Use This Book? 3 1.4 Why is It All in Python™, Anyway? 4 1.5 Example Code and Datasets 4 1.6 Parting Words 5 Part I The Stuff You’ll Always Use 7 2 The Data Science Road Map 9 2.1 Frame the Problem 10 2.2 Understand the Data: Basic Questions 11 2.3 Understand the Data: Data Wrangling 12 2.4 Understand the Data: Exploratory Analysis 13 2.5 Extract Features 14 2.6 Model 15 2.7 Present Results 15 2.8 Deploy Code 16 2.9 Iterating 16 2.10 Glossary 17 3 Programming Languages 19 3.1 Why Use a Programming Language? What are the Other Options? 19 3.2 A Survey of Programming Languages for Data Science 20 3.2.1 Python 20 3.2.2 R 21 3.2.3 MATLAB® and Octave 21 3.2.4 SAS® 21 3.2.5 Scala® 22 3.3 Python Crash Course 22 3.3.1 A Note on Versions 22 3.3.2 “Hello World” Script 23 3.3.3 More Complicated Script 23 3.3.4 Atomic Data Types 26 3.4 Strings 27 3.4.1 Comments and Docstrings 28 3.4.2 Complex Data Types 29 3.4.3 Lists 29 3.4.4 Strings and Lists 30 3.4.5 Tuples 31 3.4.6 Dictionaries 31 3.4.7 Sets 32 3.5 Defining Functions 32 3.5.1 For Loops and Control Structures 33 3.5.2 A Few Key Functions 34 3.5.3 Exception Handling 35 3.5.4 Libraries 35 3.5.5 Classes and Objects 35 3.5.6 GOTCHA: Hashable and Unhashable Types 36 3.6 Python’s Technical Libraries 37 3.6.1 Data Frames 38 3.6.2 Series 39 3.6.3 Joining and Grouping 40 3.7 Other Python Resources 42 3.8 Further Reading 42 3.9 Glossary 43 3a Interlude: My Personal Toolkit 45 4 Data Munging: String Manipulation, Regular Expressions, and Data Cleaning 47 4.1 The Worst Dataset in the World 48 4.2 How to Identify Pathologies 48 4.3 Problems with Data Content 49 4.3.1 Duplicate Entries 49 4.3.2 Multiple Entries for a Single Entity 49 4.3.3 Missing Entries 49 4.3.4 NULLs 50 4.3.5 Huge Outliers 50 4.3.6 Out‐of‐Date Data 50 4.3.7 Artificial Entries 50 4.3.8 Irregular Spacings 51 4.4 Formatting Issues 51 4.4.1 Formatting is Irregular between Different Tables/Columns 51 4.4.2 Extra Whitespace 51 4.4.3 Irregular Capitalization 52 4.4.4 Inconsistent Delimiters 52 4.4.5 Irregular NULL Format 52 4.4.6 Invalid Characters 52 4.4.7 Weird or Incompatible Datetimes 52 4.4.8 Operating System Incompatibilities 53 4.4.9 Wrong Software Versions 53 4.5 Example Formatting Script 54 4.6 Regular Expressions 55 4.6.1 Regular Expression Syntax 56 4.7 Life in the Trenches 60 4.8 Glossary 60 5 Visualizations and Simple Metrics 61 5.1 A Note on Python’s Visualization Tools 62 5.2 Example Code 62 5.3 Pie Charts 63 5.4 Bar Charts 65 5.5 Histograms 66 5.6 Means, Standard Deviations, Medians, and Quantiles 69 5.7 Boxplots 70 5.8 Scatterplots 72 5.9 Scatterplots with Logarithmic Axes 74 5.10 Scatter Matrices 76 5.11 Heatmaps 77 5.12 Correlations 78 5.13 Anscombe’s Quartet and the Limits of Numbers 80 5.14 Time Series 81 5.15 Further Reading 85 5.16 Glossary 85 6 Machine Learning Overview 87 6.1 Historical Context 88 6.2 Supervised versus Unsupervised 89 6.3 Training Data, Testing Data, and the Great Boogeyman of Overfitting 89 6.4 Further Reading 91 6.5 Glossary 91 7 Interlude: Feature Extraction Ideas 93 7.1 Standard Features 93 7.2 Features That Involve Grouping 94 7.3 Preview of More Sophisticated Features 95 7.4 Defining the Feature You Want to Predict 95 8 Machine Learning Classification 97 8.1 What is a Classifier, and What Can You Do with It? 97 8.2 A Few Practical Concerns 98 8.3 Binary versus Multiclass 99 8.4 Example Script 99 8.5 Specific Classifiers 101 8.5.1 Decision Trees 101 8.5.2 Random Forests 103 8.5.3 Ensemble Classifiers 104 8.5.4 Support Vector Machines 105 8.5.5 Logistic Regression 108 8.5.6 Lasso Regression 110 8.5.7 Naive Bayes 110 8.5.8 Neural Nets 112 8.6 Evaluating Classifiers 114 8.6.1 Confusion Matrices 114 8.6.2 ROC Curves 115 8.6.3 Area under the ROC Curve 116 8.7 Selecting Classification Cutoffs 117 8.7.1 Other Performance Metrics 118 8.7.2 Lift–Reach Curves 118 8.8 Further Reading 119 8.9 Glossary 119 9 Technical Communication and Documentation 121 9.1 Several Guiding Principles 122 9.1.1 Know Your Audience 122 9.1.2 Show Why It Matters 122 9.1.3 Make It Concrete 123 9.1.4 A Picture is Worth a Thousand Words 123 9.1.5 Don’t Be Arrogant about Your Tech Knowledge 124 9.1.6 Make It Look Decent 124 9.2 Slide Decks 124 9.2.1 C.R.A.P. Design 125 9.2.2 A Few Tips and Rules of Thumb 127 9.3 Written Reports 128 9.4 Speaking: What Has Worked for Me 130 9.5 Code Documentation 131 9.6 Further Reading 132 9.7 Glossary 132 Part II Stuff You Still Need to Know 133 10 Unsupervised Learning: Clustering and Dimensionality Reduction 135 10.1 The Curse of Dimensionality 136 10.2 Example: Eigenfaces for Dimensionality Reduction 138 10.3 Principal Component Analysis and Factor Analysis 140 10.4 Skree Plots and Understanding Dimensionality 142 10.5 Factor Analysis 143 10.6 Limitations of PCA 143 10.7 Clustering 144 10.7.1 Real‐World Assessment of Clusters 144 10.7.2 k‐Means Clustering 145 10.7.3 Gaussian Mixture Models 146 10.7.4 Agglomerative Clustering 147 10.7.5 Evaluating Cluster Quality 148 10.7.6 SiIhouette Score 148 10.7.7 Rand Index and Adjusted Rand Index 149 10.7.8 Mutual Information 150 10.8 Further Reading 151 10.9 Glossary 151 11 Regression 153 11.1 Example: Predicting Diabetes Progression 153 11.2 Least Squares 156 11.3 Fitting Nonlinear Curves 157 11.4 Goodness of Fit: R2 and Correlation 159 11.5 Correlation of Residuals 160 11.6 Linear Regression 161 11.7 LASSO Regression and Feature Selection 162 11.8 Further Reading 164 11.9 Glossary 164 12 Data Encodings and File Formats 165 12.1 Typical File Format Categories 165 12.1.1 Text Files 166 12.1.2 Dense Numerical Arrays 166 12.1.3 Program‐Specific Data Formats 166 12.1.4 Compressed or Archived Data 166 12.2 CSV Files 167 12.3 JSON Files 168 12.4 XML Files 170 12.5 HTML Files 172 12.6 Tar Files 174 12.7 GZip Files 175 12.8 Zip Files 175 12.9 Image Files: Rasterized, Vectorized, and/or Compressed 176 12.10 It’s All Bytes at the End of the Day 177 12.11 Integers 178 12.12 Floats 179 12.13 Text Data 180 12.14 Further Reading 183 12.15 Glossary 183 13 Big Data 185 13.1 What is Big Data? 185 13.2 Hadoop: The File System and the Processor 187 13.3 Using HDFS 188 13.4 Example PySpark Script 189 13.5 Spark Overview 190 13.6 Spark Operations 192 13.7 Two Ways to Run PySpark 193 13.8 Configuring Spark 194 13.9 Under the Hood 195 13.10 Spark Tips and Gotchas 196 13.11 The MapReduce Paradigm 197 13.12 Performance Considerations 199 13.13 Further Reading 200 13.14 Glossary 200 14 Databases 203 14.1 Relational Databases and MySQL® 204 14.1.1 Basic Queries and Grouping 204 14.1.2 Joins 207 14.1.3 Nesting Queries 208 14.1.4 Running MySQL and Managing the DB 209 14.2 Key-Value Stores 210 14.3 Wide Column Stores 211 14.4 Document Stores 211 14.4.1 MongoDB® 212 14.5 Further Reading 214 14.6 Glossary 214 15 Software Engineering Best Practices 217 15.1 Coding Style 217 15.2 Version Control and Git for Data Scientists 220 15.3 Testing Code 222 15.3.1 Unit Tests 223 15.3.2 Integration Tests 224 15.4 Test-Driven Development 225 15.5 AGILE Methodology 225 15.6 Further Reading 226 15.7 Glossary 226 16 Natural Language Processing 229 16.1 Do I Even Need NLP? 229 16.2 The Great Divide: Language versus Statistics 230 16.3 Example: Sentiment Analysis on Stock Market Articles 230 16.4 Software and Datasets 232 16.5 Tokenization 233 16.6 Central Concept: Bag‐of‐Words 233 16.7 Word Weighting: TF‐IDF 235 16.8 n‐Grams 235 16.9 Stop Words 236 16.10 Lemmatization and Stemming 236 16.11 Synonyms 237 16.12 Part of Speech Tagging 237 16.13 Common Problems 238 16.13.1 Search 238 16.13.2 Sentiment Analysis 239 16.13.3 Entity Recognition and Topic Modeling 240 16.14 Advanced NLP: Syntax Trees, Knowledge, and Understanding 240 16.15 Further Reading 241 16.16 Glossary 242 17 Time Series Analysis 243 17.1 Example: Predicting Wikipedia Page Views 244 17.2 A Typical Workflow 247 17.3 Time Series versus Time-Stamped Events 248 17.4 Resampling an Interpolation 249 17.5 Smoothing Signals 251 17.6 Logarithms and Other Transformations 252 17.7 Trends and Periodicity 252 17.8 Windowing 253 17.9 Brainstorming Simple Features 254 17.10 Better Features: Time Series as Vectors 255 17.11 Fourier Analysis: Sometimes a Magic Bullet 256 17.12 Time Series in Context: The Whole Suite of Features 259 17.13 Further Reading 259 17.14 Glossary 260 18 Probability 261 18.1 Flipping Coins: Bernoulli Random Variables 261 18.2 Throwing Darts: Uniform Random Variables 263 18.3 The Uniform Distribution and Pseudorandom Numbers 263 18.4 Nondiscrete, Noncontinuous Random Variables 265 18.5 Notation, Expectations, and Standard Deviation 267 18.6 Dependence, Marginal and Conditional Probability 268 18.7 Understanding the Tails 269 18.8 Binomial Distribution 271 18.9 Poisson Distribution 272 18.10 Normal Distribution 272 18.11 Multivariate Gaussian 273 18.12 Exponential Distribution 274 18.13 Log-Normal Distribution 276 18.14 Entropy 277 18.15 Further Reading 279 18.16 Glossary 279 19 Statistics 281 19.1 Statistics in Perspective 281 19.2 Bayesian versus Frequentist: Practical Tradeoffs and Differing Philosophies 282 19.3 Hypothesis Testing: Key Idea and Example 283 19.4 Multiple Hypothesis Testing 285 19.5 Parameter Estimation 286 19.6 Hypothesis Testing: t-Test 287 19.7 Confidence Intervals 290 19.8 Bayesian Statistics 291 19.9 Naive Bayesian Statistics 293 19.10 Bayesian Networks 293 19.11 Choosing Priors: Maximum Entropy or Domain Knowledge 294 19.12 Further Reading 295 19.13 Glossary 295 20 Programming Language Concepts 297 20.1 Programming Paradigms 297 20.1.1 Imperative 298 20.1.2 Functional 298 20.1.3 Object‐Oriented 301 20.2 Compilation and Interpretation 305 20.3 Type Systems 307 20.3.1 Static versus Dynamic Typing 308 20.3.2 Strong versus Weak Typing 308 20.4 Further Reading 309 20.5 Glossary 309 21 Performance and Computer Memory 311 21.1 Example Script 311 21.2 Algorithm Performance and Big‐O Notation 314 21.3 Some Classic Problems: Sorting a List and Binary Search 315 21.4 Amortized Performance and Average Performance 318 21.5 Two Principles: Reducing Overhead and Managing Memory 320 21.6 Performance Tip: Use Numerical Libraries When Applicable 322 21.7 Performance Tip: Delete Large Structures You Don’t Need 323 21.8 Performance Tip: Use Built‐In Functions When Possible 324 21.9 Performance Tip: Avoid Superfluous Function Calls 324 21.10 Performance Tip: Avoid Creating Large New Objects 325 21.11 Further Reading 325 21.12 Glossary 325 Part III Specialized or Advanced Topics 327 22 Computer Memory and Data Structures 329 22.1 Virtual Memory, the Stack, and the Heap 329 22.2 Example C Program 330 22.3 Data Types and Arrays in Memory 330 22.4 Structs 332 22.5 Pointers, the Stack, and the Heap 333 22.6 Key Data Structures 337 22.6.1 Strings 337 22.6.2 Adjustable‐Size Arrays 338 22.6.3 Hash Tables 339 22.6.4 Linked Lists 340 22.6.5 Binary Search Trees 342 22.7 Further Reading 343 22.8 Glossary 343 23 Maximum Likelihood Estimation and Optimization 345 23.1 Maximum Likelihood Estimation 345 23.2 A Simple Example: Fitting a Line 346 23.3 Another Example: Logistic Regression 348 23.4 Optimization 348 23.5 Gradient Descent and Convex Optimization 350 23.6 Convex Optimization 353 23.7 Stochastic Gradient Descent 355 23.8 Further Reading 355 23.9 Glossary 356 24 Advanced Classifiers 357 24.1 A Note on Libraries 358 24.2 Basic Deep Learning 358 24.3 Convolutional Neural Networks 361 24.4 Different Types of Layers. What the Heck is a Tensor? 362 24.5 Example: The MNIST Handwriting Dataset 363 24.6 Recurrent Neural Networks 366 24.7 Bayesian Networks 367 24.8 Training and Prediction 369 24.9 Markov Chain Monte Carlo 369 24.10 PyMC Example 370 24.11 Further Reading 373 24.12 Glossary 373 25 Stochastic Modeling 375 25.1 Markov Chains 375 25.2 Two Kinds of Markov Chain, Two Kinds of Questions 377 25.3 Markov Chain Monte Carlo 379 25.4 Hidden Markov Models and the Viterbi Algorithm 380 25.5 The Viterbi Algorithm 382 25.6 Random Walks 384 25.7 Brownian Motion 384 25.8 ARIMA Models 385 25.9 Continuous‐Time Markov Processes 386 25.10 Poisson Processes 387 25.11 Further Reading 388 25.12 Glossary 388 25a Parting Words: Your Future as a Data Scientist 391 Index 393

Autorenportrait

FIELD CADY is the data scientist at the Allen Institute for Artificial Intelligence, where he develops tools that use machine learning to mine scientific literature. He has also worked at Google and several Big Data startups. He has a BS in physics and math from Stanford University, and an MS in computer science from Carnegie Mellon.

Back cover copy

A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master the discipline Finding a good data scientist has been likened to hunting for a unicorn: the required combination of technical skills is simply very hard to find in one person. In addition, good data science is not just rote application of trainable skill sets; it requires the ability to think flexibly about all these areas and understand the connections between them. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. Unlike many analytics books, computer science and software engineering are given extensive coverage since they play such a central role in the daily work of a data scientist. The author also describes classic machine learning algorithms, from their mathematical foundations to real-world applications. Visualization tools are reviewed, and their central importance in data science is highlighted. Classical statistics is addressed to help readers think critically about the interpretation of data and its common pitfalls. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter, and all topics are explained in the context of solving real-world data problems. The book also features: <ul><li>Extensive sample code and tutorials using Python™ along with its technical libraries</li><li>Core technologies of “Big Data,” including their strengths and limitations and how they can be used to solve real-world problems</li><li>Coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presented, it is done in an intuitive way to encourage critical thinking and creativity </li><li>A wide variety of case studies from industry</li><li>Practical advice on the realities of being a data scientist today, including the overall workflow, where time is spent, the types of datasets worked on, and the skill sets needed</li></ul> The Data Science Handbook is an ideal resource for data analysis methodology and big data software tools. The book is appropriate for people who want to practice data science, but lack the required skill sets. This includes software professionals who need to better understand analytics and statisticians who need to understand software. Modern data science is a unified discipline, and it is presented as such. This book is also an appropriate reference for researchers and entry-level graduate students who need to learn real-world analytics and expand their skill set.