Data Analysis with Open Source Tools: A Hands-On Guide for Programmers and Data Scientists

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"O'Reilly Media, Inc.", Nov 11, 2010 - Computers - 540 pages
2 Reviews
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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 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

 

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User Review - Flag as inappropriate

This book discusses how to make models and mine data. The author provides caveats that that appearances often override data, decision makers use data for support rather than reasoning, ethics outweigh data, and many things cannot be measured yet. Realtime means right this minute rather than up to date. Data is cleaned prior to analysis. There are a couple of dozen software tools discussed. It uses math examples rather than code, for data analysis and calculus, and has a statistics refresher. There are interesting styles of plots. Some case studies are detailed. Each chapter has workshop exercises, an intermezzo for related topics, and further reading. There are four parts, eighteen chapters and three appendices. The reader interested in data filtering might need additional sources beyond the time series presented here. 

LibraryThing Review

User Review  - deldevries - LibraryThing

Excellent ideas for data analysis concepts. Useful ideas. Read full review

Selected pages

Contents

Introduction
1
Looking at Data
9
Modeling Data
139
Mining Data
265
Using Data
359
Appendix A
435
Appendix B
447
Appendix C
485
Index
499
Copyright

Common terms and phrases

actual algorithm applications approximation argument average basic Bayesian behavior Berkeley DB Bernoulli trials bootstrap box plot calculate Central Limit Theorem Chapter classifier clusters coefficient coordinate correlation cost cumulative distribution function curve data analysis data points data set database density develop discussed distance distribution elements error estimate evaluate example expect exponential expression factor Figure function Gaussian graph graphics histogram implementation important input instance interactive k-means algorithm kernel likelihood function linear LOESS logarithm look mathematical matplotlib matrix mean measure methods metrics NumPy open source original outliers overall parameter percent power-law principal component analysis probability problem Python quantity queue random sample scale scatter plot server simple simulation single smooth specific SQLite standard deviation statistics techniques Theorem tool total number typical values variables vector width zero

Bibliographic information

QR code for Data Analysis with Open Source Tools
TitleData Analysis with Open Source Tools: A Hands-On Guide for Programmers and Data Scientists
AuthorPhilipp K. Janert
Publisher"O'Reilly Media, Inc.", 2010
ISBN1449396658, 9781449396657
Length540 pages
Subjects ›  › 

Computers / Data Science / Data Analytics
Computers / Languages / Python
Computers / Mathematical & Statistical Software
  
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