Data Analysis with Open Source Tools

Preface

1. Introduction
-Data Analysis
-What's in This Book
-What's with the Workshops?
-What's with the Math?
-What You'll Need
-What's Missing

Part 1:Graphics: Looking at Data
2. A Single Variable: Shape and Distribution
-Dot and Jitter Plots
-Histograms and Kernel Density Estimates
-The Cumulative Distribution Function
-Rank-Order Plots and Lift Charts
-Only When Appropriate: Summary Statistics and Box Plots
-Workshop: NumPy
-Further Reading

3. Two Variables: Establishing Relationships
-Scatter Plots
-Conquering Noise: Smoothing
-Logarithmic Plots
-Banking
-Linear Regression and All That
-Showing What's Important
-Graphical Analysis and Presentation Graphics
-Workshop: matplotlib
-Further Reading

4. Time As a Variable: Time-Series Analysis
-Examples
-The Task
-Smoothing
-Don't Overlook the Obvious!
-The Correlation Function
-Optional: Filters and Convolutions
-Workshop: scipy.signal
-Further Reading

5. More Than Two Variables: Graphical Multivariate Analysis
-False-Color Plots
-A Lot at a Glance: Multiplots
-Composition Problems
-Novel Plot Types
-Interactive Explorations
-Workshop: Tools for Multivariate Graphics
-Further Reading

6. Intermezzo: A Data Analysis Session
-A Data Analysis Session
-Workshop: gnuplot
-Further Reading

Part 2: Analytics: Modeling Data
7. Guesstimation and the Back of the Envelope
-Principles of Guesstimation
-How Good Are Those Numbers?
-Optional: A Closer Look at Perturbation Theory and Error Propagation
-Workshop: The Gnu Scientific Library (GSL)
-Further Reading

8. Models from Scaling Arguments
-Models
-Arguments from Scale
-Mean-Field Approximations
-Common Time-Evolution Scenarios
-Case Study: How Many Servers Are Best?
-Why Modeling?
-Workshop: Sage
-Further Reading

9. Arguments from Probability Models
-The Binomial Distribution and Bernoulli Trials
-The Gaussian Distribution and the Central Limit Theorem
-Power-Law Distributions and Non-Normal Statistics
-Other Distributions
-Optional: Case Study-Unique Visitors over Time
-Workshop: Power-Law Distributions
-Further Reading

10. What You Really Need to Know About Classical Statistics
-Genesis
-Statistics Defined
-Statistics Explained
-Controlled Experiments Versus Observational Studies
-Optional: Bayesian Statistics-The Other Point of View
-Workshop: R
-Further Reading

11. Intermezzo: Mythbusting-Bigfoot, Least Squares, and All That
-How to Average Averages
-The Standard Deviation
-Least Squares
-Further Reading

Part 3: Computation: Mining Data
12. Simulations
-A Warm-Up Question
-Monte Carlo Simulations
-Resampling Methods
-Workshop: Discrete Event Simulations with SimPy
-Further Reading

13. Finding Clusters
-What Constitutes a Cluster?
-Distance and Similarity Measures
-Clustering Methods
-Pre- and Postprocessing
-Other Thoughts
-A Special Case: Market Basket Analysis
-A Word of Warning
-Workshop: Pycluster and the C Clustering Library
-Further Reading

14. Seeing the Forest for the Trees: Finding Important Attributes
-Principal Component Analysis
-Visual Techniques
-Kohonen Maps
-Workshop: PCA with R
-Further Reading

15. Intermezzo: When More Is Different
-A Horror Story
-Some Suggestions
-What About Map/Reduce?
-Workshop: Generating Permutations
-Further Reading

Part 4: Applications: Using Data
16. Reporting, Business Intelligence, and Dashboards
-Business Intelligence
-Corporate Metrics and Dashboards
-Data Quality Issues
-Workshop: Berkeley DB and SQLite
-Further Reading

17. Financial Calculations and Modeling
-The Time Value of Money
-Uncertainty in Planning and Opportunity Costs
-Cost Concepts and Depreciation
-Should You Care?
-Is This All That Matters?
-Workshop: The Newsvendor Problem
-Further Reading

18. Predictive Analytics
-Topics in Predictive Analytics
-Some Classification Terminology
-Algorithms for Classification
-The Process
-The Secret Sauce
-The Nature of Statistical Learning
-Workshop: Two Do-It-Yourself Classifiers
-Further Reading

19. Epilogue: Facts Are Not Reality

Appendix A: Programming Environments for Scientific Computation and Data
Appendix B: Results from Calculus
Appendix C: Working with Data
Appendix D: About the Author

Index