Philipp K. Janert is Chief Consultant at Principal Value, LLC. He has worked for small start-ups and in large corporate environments, both in the US and overseas, including several years at Amazon.com, where he initiated and led several projects to improve Amazon's order fulfillment processes. Philipp K. Janert has written about software and software development for the O'Reilly Network, IBM developerWorks, IEEE Software, and Linux Magazine. He holds a Ph.D. in Theoretical Physics from the University of Washington. Visit his website at www.principal-value.com.
Data Analysis with Open Source Tools: A Hands-On Guide for Programmers and Data Scientistsby Philipp K. Janert
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
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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Excellent and comprehensive overview of data analysis, graphing and data analytical thinking using multiple open source tools (including Python, R, Gnuplot, etc.). I did not find it overwhelming although I have limited applied math and coding experience.
if some one uses R for one example why use python for another