Philipp K. Janert

Data Analysis with Open Source Tools

A Hands-On Guide for Programmers and Data Scientists

• Broschiertes Buch

Produktbeschreibung

Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications.

Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you.
Use graphics to describe data with one, two, or dozens of variables
Develop conceptual models using back-of-the-envelope calculations, as well asscaling and probability arguments
Mine data with computationally intensive methods such as simulation and clustering
Make your conclusions understandable through reports, dashboards, and other metrics programs
Understand financial calculations, including the time-value of money
Use dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situations
Become familiar with different open source programming environments for data analysis

"Finally, a concise reference for understanding how to conquer piles of data."--Austin King, Senior Web Developer, Mozilla

"An indispensable text for aspiring data scientists."--Michael E. Driscoll, CEO/Founder, Dataspora

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Verlag: O'Reilly Media
• Seitenzahl: 530
• Erscheinungstermin: 28. Dezember 2010
• Englisch
• Abmessung: 233mm x 180mm x 30mm
• Gewicht: 868g
• ISBN-13: 9780596802356
• ISBN-10: 0596802358
• Artikelnr.: 29611575

Autorenporträt

After previous careers in physics and softwaredevelopment, Philipp K. Janert currentlyprovides consulting services for data analysis,algorithm development, and mathematical modeling.He has worked for small start-ups and in largecorporate environments, both in the U.S. andoverseas. He prefers simple solutions that workto complicated ones that don't, and thinks thatpurpose is more important than process. Philippis the author of "Gnuplot in Action - UnderstandingData with Graphs" (Manning Publications), and haswritten for the O'Reilly Network, IBM developerWorks,and IEEE Software. He is named inventor on a handfulof patents, and is an occasional contributor to CPAN.He holds a Ph.D. in theoretical physics from theUniversity of Washington. Visit his company websiteat www.principal-value.com.

Inhaltsangabe

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