Data Analysis with Open Source Tools

A Hands-On Guide for Programmers and Data Secrets

Paperback Engels 2011 1e druk 9780596802356
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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 as scaling 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

Specificaties

ISBN13:9780596802356
Taal:Engels
Bindwijze:paperback
Aantal pagina's:509
Uitgever:O'Reilly
Druk:1
Hoofdrubriek:IT-management / ICT

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Over Philipp Janert

Philipp K. Janert provides consulting services for data analysis and mathematical modeling, drawing on his previous careers as physcist and software engineer. He is the author of Gnuplot in Action: Understanding Data with Graphs (Manning Publications) and has written for the O'Reilly Network, IBM developerWorks, and IEEE Software. He holds a Ph.D. in theoretical physics from the University of Washington.

Andere boeken door Philipp Janert

Inhoudsopgave

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
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