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• Data Analysis with Open Source Tools

• By: Philipp K. Janert

• Publisher: O'Reilly Media, Inc.

• Pub. Date: November 18, 2010

• Print ISBN-13: 978-0-596-80235-6

• Pages in Print Edition: 538

• Amazon.com® Reviews

• Subscriber Rating: [0 Ratings]

• Overview
• Table of Contents
• Extras
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Real World Data Analysis shows you how you think about data and the results you want to achieve with it. Author Philipp Janert teaches you how to approach data analysis problems and extract all the information available from your data. But this book isn't just about academic topics: it's the only book on data that stresses the seat-of-the-pants knowledge that leads you to the right approach in the first place. There are lots of people who can apply a formula. Janert shows you how to look at a result and know whether it's meaningful.

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Table of Contents

 

Copyright

Preface

Download Chapter
1 TokenChapter 1. Introduction

Section 1.1. Data Analysis

Section 1.2. What’s in This Book

Section 1.3. What’s with the Workshops?

Section 1.4. What’s with the Math?

Section 1.5. What You’ll Need

Section 1.6. What’s Missing

Part 1: Graphics: Looking at Data

Download Chapter
1 TokenChapter 2. A Single Variable: Shape and Distribution

Section 2.1. Dot and Jitter Plots

Section 2.2. Histograms and Kernel Density Estimates

Section 2.3. The Cumulative Distribution Function

Section 2.4. Rank-Order Plots and Lift Charts

Section 2.5. Only When Appropriate: Summary Statistics and Box Plots

Section 2.6. Workshop: NumPy

Section 2.7. Further Reading

Download Chapter
1 TokenChapter 3. Two Variables: Establishing Relationships

Section 3.1. Scatter Plots

Section 3.2. Conquering Noise: Smoothing

Section 3.3. Logarithmic Plots

Section 3.4. Banking

Section 3.5. Linear Regression and All That

Section 3.6. Showing What’s Important

Section 3.7. Graphical Analysis and Presentation Graphics

Section 3.8. Workshop: matplotlib

Section 3.9. Further Reading

Chapter 4. Time As a Variable: Time-Series Analysis

Section 4.1. Examples

Section 4.2. The Task

Section 4.3. Smoothing

Section 4.4. Don’t Overlook the Obvious!

Section 4.5. The Correlation Function

Section 4.6. Optional: Filters and Convolutions

Section 4.7. Workshop: scipy.signal

Section 4.8. Further Reading

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1 TokenChapter 5. More Than Two Variables: Graphical Multivariate Analysis

Section 5.1. False-Color Plots

Section 5.2. A Lot at a Glance: Multiplots

Section 5.3. Composition Problems

Section 5.4. Novel Plot Types

Section 5.5. Interactive Explorations

Section 5.6. Workshop: Tools for Multivariate Graphics

Section 5.7. Further Reading

Download Chapter
1 TokenChapter 6. Intermezzo: A Data Analysis Session

Section 6.1. A Data Analysis Session

Section 6.2. Workshop: gnuplot

Section 6.3. Further Reading

Part 2: Analytics: Modeling Data

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1 TokenChapter 7. Guesstimation and the Back of the Envelope

Section 7.1. Principles of Guesstimation

Section 7.2. How Good Are Those Numbers?

Section 7.3. Optional: A Closer Look at Perturbation Theory and Error Propagation

Section 7.4. Workshop: The Gnu Scientific Library (GSL)

Section 7.5. Further Reading

Download Chapter
1 TokenChapter 8. Models from Scaling Arguments

Section 8.1. Models

Section 8.2. Arguments from Scale

Section 8.3. Mean-Field Approximations

Section 8.4. Common Time-Evolution Scenarios

Section 8.5. Case Study: How Many Servers Are Best?

Section 8.6. Why Modeling?

Section 8.7. Workshop: Sage

Section 8.8. Further Reading

Download Chapter
1 TokenChapter 9. Arguments from Probability Models

Section 9.1. The Binomial Distribution and Bernoulli Trials

Section 9.2. The Gaussian Distribution and the Central Limit Theorem

Section 9.3. Power-Law Distributions and Non-Normal Statistics

Section 9.4. Other Distributions

Section 9.5. Optional: Case Study—Unique Visitors over Time

Section 9.6. Workshop: Power-Law Distributions

Section 9.7. Further Reading

Download Chapter
1 TokenChapter 10. What You Really Need to Know About Classical Statistics

Section 10.1. Genesis

Section 10.2. Statistics Defined

Section 10.3. Statistics Explained

Section 10.4. Controlled Experiments Versus Observational Studies

Section 10.5. Optional: Bayesian Statistics—The Other Point of View

Section 10.6. Workshop: R

Section 10.7. Further Reading

Download Chapter
1 TokenChapter 11. Intermezzo: Mythbusting—Bigfoot, Least Squares, and All That

Section 11.1. How to Average Averages

Section 11.2. The Standard Deviation

Section 11.3. Least Squares

Section 11.4. Further Reading

Part 3: Computation: Mining Data

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1 TokenChapter 12. Simulations

Section 12.1. A Warm-Up Question

Section 12.2. Monte Carlo Simulations

Section 12.3. Resampling Methods

Section 12.4. Workshop: Discrete Event Simulations with SimPy

Section 12.5. Further Reading

Download Chapter
1 TokenChapter 13. Finding Clusters

Section 13.1. What Constitutes a Cluster?

Section 13.2. Distance and Similarity Measures

Section 13.3. Clustering Methods

Section 13.4. Pre- and Postprocessing

Section 13.5. Other Thoughts

Section 13.6. A Special Case: Market Basket Analysis

Section 13.7. A Word of Warning

Section 13.8. Workshop: Pycluster and the C Clustering Library

Section 13.9. Further Reading

Download Chapter
1 TokenChapter 14. Seeing the Forest for the Trees: Finding Important Attributes

Section 14.1. Principal Component Analysis

Section 14.2. Visual Techniques

Section 14.3. Kohonen Maps

Section 14.4. Workshop: PCA with R

Section 14.5. Further Reading

Download Chapter
1 TokenChapter 15. Intermezzo: When More Is Different

Section 15.1. A Horror Story

Section 15.2. Some Suggestions

Section 15.3. What About Map/Reduce?

Section 15.4. Workshop: Generating Permutations

Section 15.5. Further Reading

Part 4: Applications: Using Data

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1 TokenChapter 16. Reporting, Business Intelligence, and Dashboards

Section 16.1. Business Intelligence

Section 16.2. Corporate Metrics and Dashboards

Section 16.3. Data Quality Issues

Section 16.4. Workshop: Berkeley DB and SQLite

Section 16.5. Further Reading

Download Chapter
1 TokenChapter 17. Financial Calculations and Modeling

Section 17.1. The Time Value of Money

Section 17.2. Uncertainty in Planning and Opportunity Costs

Section 17.3. Cost Concepts and Depreciation

Section 17.4. Should You Care?

Section 17.5. Is This All That Matters?

Section 17.6. Workshop: The Newsvendor Problem

Section 17.7. Further Reading

Download Chapter
1 TokenChapter 18. Predictive Analytics

Section 18.1. Topics in Predictive Analytics

Section 18.2. Some Classification Terminology

Section 18.3. Algorithms for Classification

Section 18.4. The Process

Section 18.5. The Secret Sauce

Section 18.6. The Nature of Statistical Learning

Section 18.7. Workshop: Two Do-It-Yourself Classifiers

Section 18.8. Further Reading

Download Chapter
1 TokenChapter 19. Epilogue: Facts Are Not Reality

Download Chapter
1 TokenAppendix A. Programming Environments for Scientific Computation and Data Analysis

Section A.1. Software Tools

Section A.2. A Catalog of Scientific Software

Section A.3. Writing Your Own

Section A.4. Further Reading

Download Chapter
1 TokenAppendix B. Results from Calculus

Section B.1. Common Functions

Section B.2. Calculus

Section B.3. Useful Tricks

Section B.4. Notation and Basic Math

Section B.5. Where to Go from Here

Section B.6. Further Reading

Download Chapter
1 TokenAppendix C. Working with Data

Section C.1. Sources for Data

Section C.2. Cleaning and Conditioning

Section C.3. Sampling

Section C.4. Data File Formats

Section C.5. The Care and Feeding of Your Data Zoo

Section C.6. Skills

Section C.7. Terminology

Section C.8. Further Reading

Download Chapter
1 TokenAppendix D. About the Author

Colophon

Index

Extras

The publisher has provided additional content related to this title.

Description	Content
Visit the catalog page for Data Analysis with Open Source Tools	Catalog Page
Visit the errata page for Data Analysis with Open Source Tools	Errata
Download the supplemental electronic content for Data Analysis with Open Source Tools	Supplemental Content

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