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;Preface; Before We Begin; Conventions Used in This Book; Using Code Examples; Safari® Books Online; How to Contact Us; Acknowledgments;
Chapter 1: Introduction;
1.1 Data Analysis;
1.2 What''s in This Book;
1.3 What''s with the Workshops?;
1.4 What''s with the Math?;
1.5 What You''ll Need;
1.6 What''s Missing;Graphics: Looking at Data;
Chapter 2: A Single Variable: Shape and Distribution;
2.1 Dot and Jitter Plots;
2.2 Histograms and Kernel Density Estimates;
2.3 The Cumulative Distribution Function;
2.4 Rank-Order Plots and Lift Charts;
2.5 Only When Appropriate: Summary Statistics and Box Plots;
2.6 Workshop: NumPy;
2.7 Further Reading;
Chapter 3: Two Variables: Establishing Relationships;
3.1 Scatter Plots;
3.2 Conquering Noise: Smoothing;
3.3 Logarithmic Plots;
3.4 Banking;
3.5 Linear Regression and All That;
3.6 Showing What''s Important;
3.7 Graphical Analysis and Presentation Graphics;
3.8 Workshop: matplotlib;
3.9 Further Reading;
Chapter 4: Time As a Variable: Time-Series Analysis;
4.1 Examples;
4.2 The Task;
4.3 Smoothing;
4.4 Don''t Overlook the Obvious!;
4.5 The Correlation Function;
4.6 Optional: Filters and Convolutions;
4.7 Workshop: scipy.signal;
4.8 Further Reading;
Chapter 5: More Than Two Variables: Graphical Multivariate Analysis;
5.1 False-Color Plots;
5.2 A Lot at a Glance: Multiplots;
5.3 Composition Problems;
5.4 Novel Plot Types;
5.5 Interactive Explorations;
5.6 Workshop: Tools for Multivariate Graphics;
5.7 Further Reading;
Chapter 6: Intermezzo: A Data Analysis Session;
6.1 A Data Analysis Session;
6.2 Workshop: gnuplot;
6.3 Further Reading;Analytics: Modeling Data;
Chapter 7: Guesstimation and the Back of the Envelope;
7.1 Principles of Guesstimation;
7.2 How Good Are Those Numbers?;
7.3 Optional: A Closer Look at Perturbation Theory and Error Propagation;
7.4 Workshop: The Gnu Scientific Library (GSL);
7.5 Further Reading;
Chapter 8: Models from Scaling Arguments;
8.1 Models;
8.2 Arguments from Scale;
8.3 Mean-Field Approximations;
8.4 Common Time-Evolution Scenarios;
8.5 Case Study: How Many Servers Are Best?;
8.6 Why Modeling?;
8.7 Workshop: Sage;
8.8 Further Reading;
Chapter 9: Arguments from Probability Models;
9.1 The Binomial Distribution and Bernoulli Trials;
9.2 The Gaussian Distribution and the Central Limit Theorem;
9.3 Power-Law Distributions and Non-Normal Statistics;
9.4 Other Distributions;
9.5 Optional: Case Study--Unique Visitors over Time;
9.6 Workshop: Power-Law Distributions;
9.7 Further Reading;
Chapter 10: What You Really Need to Know About Classical Statistics;
10.1 Genesis;
10.2 Statistics Defined;
10.3 Statistics Explained;
10.4 Controlled Experiments Versus Observational Studies;
10.5 Optional: Bayesian Statistics--The Other Point of View;
10.6 Workshop: R;
10.7 Further Reading;
Chapter 11: Intermezzo: Mythbusting--Bigfoot, Least Squares, and All That;
11.1 How to Average Averages;
11.2 The Standard Deviation;
11.3 Least Squares;
11.4 Further Reading;Computation: Mining Data;
Chapter 12: Simulations;
12.1 A Warm-Up Question;
12.2 Monte Carlo Simulations;
12.3 Resampling Methods;
12.4 Workshop: Discrete Event Simulations with SimPy;
12.5 Further Reading;
Chapter 13: Finding Clusters;
13.1 What Constitutes a Cluster?;
13.2 Distance and Similarity Measures;
13.3 Clustering Methods;
13.4 Pre- and Postprocessing;
13.5 Other Thoughts;
13.6 A Special Case: Market Basket Analysis;
13.7 A Word of Warning;
13.8 Workshop: Pycluster and the C Clustering Library;
13.9 Further Reading;
Chapter 14: Seeing the Forest for the Trees: Finding Important Attributes;
14.1 Principal Component Analysis;
14.2 Visual Techniques;
14.3 Kohonen Maps;
14.4 Workshop: PCA with R;
14.5 Further Reading;
Chapter 15: Intermezzo: When More Is Different;
15.1 A Horror Story;
15.2 Some Suggestions;
15.3 What About Map/Reduce?;
15.4 Workshop: Generating Permutations;
15.5 Further Reading;Applications: Using Data;
Chapter 16: Reporting, Business Intelligence, and Dashboards;
16.1 Business Intelligence;
16.2 Corporate Metrics and Dashboards;
16.3 Data Quality Issues;
16.4 Workshop: Berkeley DB and SQLite;
16.5 Further Reading;
Chapter 17: Financial Calculations and Modeling;
17.1 The Time Value of Money;
17.2 Uncertainty in Planning and Opportunity Costs;
17.3 Cost Concepts and Depreciation;
17.4 Should You Care?;
17.5 Is This All That Matters?;
17.6 Workshop: The Newsvendor Problem;
17.7 Further Reading;
Chapter 18: Predictive Analytics;
18.1 Topics in Predictive Analytics;
18.2 Some Classification Terminology;
18.3 Algorithms for Classification;
18.4 The Process;
18.5 The Secret Sauce;
18.6 The Nature of Statistical Learning;
18.7 Workshop: Two Do-It-Yourself Classifiers;
18.8 Further Reading;
Chapter 19: Epilogue: Facts Are Not Reality;Programming Environments for Scientific Computation and Data Analysis; Software Tools; A Catalog of Scientific Software; Writing Your Own; Further Reading;Results from Calculus; Common Functions; Calculus; Useful Tricks; Notation and Basic Math; Where to Go from Here; Further Reading;Working with Data; Sources for Data; Cleaning and Conditioning; Sampling; Data File Formats; The Care and Feeding of Your Data Zoo; Skills; Terminology; Further Reading;About the Author;Colophon;