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Data Analysis with Open Source Tools

Cover of Data Analysis with Open Source Tools by Philipp K. Janert Published by O'Reilly Media, Inc.
  1. Data Analysis with Open Source Tools
  2. Dedication
  3. SPECIAL OFFER: Upgrade this ebook with O’Reilly
  4. A Note Regarding Supplemental Files
  5. Preface
    1. Before We Begin
    2. Conventions Used in This Book
    3. Using Code Examples
    4. Safari® Books Online
    5. How to Contact Us
    6. Acknowledgments
  6. 1. Introduction
    1. Data Analysis
    2. What’s in This Book
    3. What’s with the Workshops?
    4. What’s with the Math?
    5. What You’ll Need
    6. What’s Missing
  7. I. Graphics: Looking at Data
    1. 2. A Single Variable: Shape and Distribution
      1. Dot and Jitter Plots
      2. Histograms and Kernel Density Estimates
      3. The Cumulative Distribution Function
      4. Rank-Order Plots and Lift Charts
      5. Only When Appropriate: Summary Statistics and Box Plots
      6. Workshop: NumPy
      7. Further Reading
    2. 3. Two Variables: Establishing Relationships
      1. Scatter Plots
      2. Conquering Noise: Smoothing
      3. Logarithmic Plots
      4. Banking
      5. Linear Regression and All That
      6. Showing What’s Important
      7. Graphical Analysis and Presentation Graphics
      8. Workshop: matplotlib
      9. Further Reading
    3. 4. Time As a Variable: Time-Series Analysis
      1. Examples
      2. The Task
      3. Smoothing
      4. Don’t Overlook the Obvious!
      5. The Correlation Function
      6. Optional: Filters and Convolutions
      7. Workshop: scipy.signal
      8. Further Reading
    4. 5. More Than Two Variables: Graphical Multivariate Analysis
      1. False-Color Plots
      2. A Lot at a Glance: Multiplots
      3. Composition Problems
      4. Novel Plot Types
      5. Interactive Explorations
      6. Workshop: Tools for Multivariate Graphics
      7. Further Reading
    5. 6. Intermezzo: A Data Analysis Session
      1. A Data Analysis Session
      2. Workshop: gnuplot
      3. Further Reading
  8. II. Analytics: Modeling Data
    1. 7. Guesstimation and the Back of the Envelope
      1. Principles of Guesstimation
      2. How Good Are Those Numbers?
      3. Optional: A Closer Look at Perturbation Theory and Error Propagation
      4. Workshop: The Gnu Scientific Library (GSL)
      5. Further Reading
    2. 8. Models from Scaling Arguments
      1. Models
      2. Arguments from Scale
      3. Mean-Field Approximations
      4. Common Time-Evolution Scenarios
      5. Case Study: How Many Servers Are Best?
      6. Why Modeling?
      7. Workshop: Sage
      8. Further Reading
    3. 9. Arguments from Probability Models
      1. The Binomial Distribution and Bernoulli Trials
      2. The Gaussian Distribution and the Central Limit Theorem
      3. Power-Law Distributions and Non-Normal Statistics
      4. Other Distributions
      5. Optional: Case Study—Unique Visitors over Time
      6. Workshop: Power-Law Distributions
      7. Further Reading
    4. 10. What You Really Need to Know About Classical Statistics
      1. Genesis
      2. Statistics Defined
      3. Statistics Explained
      4. Controlled Experiments Versus Observational Studies
      5. Optional: Bayesian Statistics—The Other Point of View
      6. Workshop: R
      7. Further Reading
    5. 11. Intermezzo: Mythbusting—Bigfoot, Least Squares, and All That
      1. How to Average Averages
      2. The Standard Deviation
      3. Least Squares
      4. Further Reading
  9. III. Computation: Mining Data
    1. 12. Simulations
      1. A Warm-Up Question
      2. Monte Carlo Simulations
      3. Resampling Methods
      4. Workshop: Discrete Event Simulations with SimPy
      5. Further Reading
    2. 13. Finding Clusters
      1. What Constitutes a Cluster?
      2. Distance and Similarity Measures
      3. Clustering Methods
      4. Pre- and Postprocessing
      5. Other Thoughts
      6. A Special Case: Market Basket Analysis
      7. A Word of Warning
      8. Workshop: Pycluster and the C Clustering Library
      9. Further Reading
    3. 14. Seeing the Forest for the Trees: Finding Important Attributes
      1. Principal Component Analysis
      2. Visual Techniques
      3. Kohonen Maps
      4. Workshop: PCA with R
      5. Further Reading
    4. 15. Intermezzo: When More Is Different
      1. A Horror Story
      2. Some Suggestions
      3. What About Map/Reduce?
      4. Workshop: Generating Permutations
      5. Further Reading
  10. IV. Applications: Using Data
    1. 16. Reporting, Business Intelligence, and Dashboards
      1. Business Intelligence
      2. Corporate Metrics and Dashboards
      3. Data Quality Issues
      4. Workshop: Berkeley DB and SQLite
      5. Further Reading
    2. 17. Financial Calculations and Modeling
      1. The Time Value of Money
      2. Uncertainty in Planning and Opportunity Costs
      3. Cost Concepts and Depreciation
      4. Should You Care?
      5. Is This All That Matters?
      6. Workshop: The Newsvendor Problem
      7. Further Reading
    3. 18. Predictive Analytics
      1. Topics in Predictive Analytics
      2. Some Classification Terminology
      3. Algorithms for Classification
      4. The Process
      5. The Secret Sauce
      6. The Nature of Statistical Learning
      7. Workshop: Two Do-It-Yourself Classifiers
      8. Further Reading
    4. 19. Epilogue: Facts Are Not Reality
  11. A. Programming Environments for Scientific Computation and Data Analysis
    1. Software Tools
      1. Scientific Software Is Different
    2. A Catalog of Scientific Software
      1. Matlab
      2. R
      3. Python
      4. What About Java?
      5. Other Players
      6. Recommendations
    3. Writing Your Own
    4. Further Reading
      1. Matlab
      2. R
      3. NumPy/SciPy
  12. B. Results from Calculus
    1. Common Functions
      1. Powers
      2. Polynomials and Rational Functions
      3. Exponential Function and Logarithm
      4. Trigonometric Functions
      5. Gaussian Function and the Normal Distribution
      6. Other Functions
      7. The Inverse of a Function
    2. Calculus
      1. Derivatives
      2. Finding Minima and Maxima
      3. Integrals
      4. Limits, Sequences, and Series
      5. Power Series and Taylor Expansion
    3. Useful Tricks
      1. The Binomial Theorem
      2. The Linear Transformation
      3. Dividing by Zero
    4. Notation and Basic Math
      1. On Reading Formulas
      2. Elementary Algebra
      3. Working with Fractions
      4. Sets, Sequences, and Series
      5. Special Symbols
      6. The Greek Alphabet
    5. Where to Go from Here
      1. On Math
    6. Further Reading
      1. Calculus
      2. Linear Algebra
      3. Complex Analysis
      4. Mindbenders
  13. C. Working with Data
    1. Sources for Data
    2. Cleaning and Conditioning
    3. Sampling
    4. Data File Formats
    5. The Care and Feeding of Your Data Zoo
    6. Skills
    7. Terminology
      1. Types of Data
      2. The Data Type Depends on the Semantics
      3. Types of Data Sets
    8. Further Reading
      1. Data Set Repositories
  14. D. About the Author
  15. Index
  16. About the Author
  17. Colophon
  18. SPECIAL OFFER: Upgrade this ebook with O’Reilly
  19. Copyright
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Chapter 18. Predictive Analytics

DATA ANALYSIS CAN TAKE MANY DIFFERENT FORMS—NOT ONLY IN THE TECHNIQUES THAT WE APPLY BUT ALSO in the kind of results that we ultimately achieve. Looking back over the material that we have covered so far, we see that the results obtained in Part I were mostly descriptive: we tried to figure out what the data was telling us and to describe it. In contrast, the results in Part II were primarily prescriptive: data was used as a guide for building models which could then be used to infer or prescribe phenomena, including effects that had not actually been observed yet. In this form of analysis, data is not used directly; instead it is used only indirectly to guide (and verify) our intuition when building models. Additionally, as I tried to stress in those chapters, we don’t just follow data blindly, but instead we try to develop an understanding of the processes that generate the data and to capture this understanding in the models we develop. The predictive power of such models derives from this understanding we develop by studying data and the circumstances in which it is generated.[30]

In this chapter, we consider yet another way to use data—we can call it predictive, since the purpose will be to make predictions about future events. What is different is that now we try to make predictions directly from the data without necessarily forming the kind of conceptual model (and the associated deeper understanding of the problem domain) as discussed in ...

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