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References

  1. Patrick Riley, 2016, "Practical advice for analysis of large, complex data sets"; Blog-post
  2. Ray Dalio, 2017, "Principles"; Book
  3. Tom Preston-Werner, 2010, "Readme Driven Development"; Blog-post
  4. Wilson et al., 2017, "Good enough practices in scientific computing"
  5. Jake Vanderplas, "Statistics for Hackers" at PyCon 2016; Slides, Video
  6. Chris Fonnesbeck, "Statistical Thinking for Data Science" at SciPy 2015; Video
  7. David Ritter, 2014, "When to Act on a Correlation, and When Not To"; HBR-article
  8. Cheng-Tao Chu, 2014, "Machine Learning Done Wrong", Blog-post
  9. Arthur Charpentier, 2015, "Variable Importance with Correlated Features", Blog-post
  10. Claudia Perlich, "All the Data and Still Not Enough!" at Strata + Hadoop World in New York, 2014, Video
  11. "The most difficult thing in data science: Politics" Blog-post
  12. William Zinsser, 2006, "On Writing Well: The Classic Guide to Writing Nonfiction", Book

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