Data Analysis with Open Source Tools
by Philipp K. Janert · first published 2010
58% recommend33% neutral8% critical
Why HN recommends it
A practical, statistics-oriented tour of analyzing small datasets with open source tools, using concrete examples. Its grounded concepts suit working engineers and newcomers to data analysis.
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1. Data Analysis with Open Source Tools This learnt me how to analyze "small" datasets which have helped me incredibly in my career. Technology wise is very out of date, but the concepts are still valid. I think it's hard to get more with…
on “Ask HN: Name 3-5 books that had the most impact on your career and knowledge?” ↗…things it gives you right out of the box are iPython (a better Python shell), Spyder (the Python version of RStudio), and Jupyter Notebooks. For learning, I'd recommend taking something like Janert's "Data Analysis with Open Source Tools" and go through chapter-by-chapter trying to figure out how to implement the various analyses in Python. That book in particular uses a different technology every chapter for its tutorial exercises,…
on “Ask HN: What are the best resources to learn Python for Data Analysis” ↗…repository. Because it's so common, you should learn some basic statistics in order to be able to better read things in context, even if you decide to use external tools or libraries to do statistics. I found 'Data Analysis with Open Source Tools' by Philipp Janert (O'Reilly) a great resource: it's language-agnostic, grounded in real-world practices, and assumes you want to solve problems using statistics (and other mathematical analysis tools)…
on “Ask HN: How to 'just code' and learn a language 'robustly'?” ↗…to learn a new language that is a bit different than what you might be used to and want to brush up on some math, I highly recommend playing with R for a while. Another book to checkout is O'Reilly's Data Analysis with Open Source Tools, which has sections on R and NumPy.
on “R in Action” ↗…questioned, but this is the common language you'll want talking to any of these people. "What is a p-value anyway?" -A very quick, non-math heavy read on avoiding magical thinking in classical statistics. "Data Analysis with Open Source Tools" -I really love this book because it steps way out of "stats 101" land and gives a lot of really great real world advice and touches on many mathematical tools that can help you understand data. The…
on “Try R — A new online course, for free” ↗…"Programming Collective Intelligence" and "Machine Learning for Hackers", which are more or less the same thing, except the first one is in Python and the second one in R (and slightly more advanced), or "Data Analysis with open source tools", which is more statistics oriented and more advanced than "Think Stats". The R books are interesting if you want to really get into R because I don't think they go into much details from a stats point…
on “Data Science Starter Kit” ↗…probably learn that first. These are three very good O'Reilly books that all use Python: - Programming Collective Intelligence: A broad and shallow survey of automated machine learning techniques. - Data Analysis with Open Source Tools: Also a survey. More focused on manual data exploration. - Python for Data Analysis: A pandas tutorial (and more). Very helpful to learn the ML tools in the python ecosystem. Fitting all that into two…
on “Ask HN: What is the best way to learn Machine Learning in Python?” ↗Philipp K. Janert, author of «Data Analysis with Open Source Tools», spends a few pages for explaining how he perceives this «difference». From his point of view Machine Learning is a fake science. Fragile, secretive and specific techniques for big problems that need…
on “Statistics vs. Machine Learning” ↗Has anyone read "Data Analysis with Open Source Tools"? http://oreilly.com/catalog/9780596802356/ I can't seem to look through the book, not even at Amazon, which I like to do before buying something.
on “O'Reilly discounts: machine learning, nltk, hadoop, sql, nosql, aws and appeng” ↗I was surprised to read someone qualified to speak on the topic suggests otherwise: In Data Analysis with Open Source Tools, P. K. Janert surveys several languages/tools, including R, Octave, and Python with NumPy/Scipy. Regarding the last, he writes: "[...] NumPy/SciPy has its own share of problems. The project has a tendency…
on “Ask HN: Isn't NumPy/SciPy used for serious academic/enterprise applications?” ↗This is an application of mean field approximation. See the O'Reilly book Data Analysis with Open Source Tools by Philipp Janert, page 175-178.
on “Richard Feynman and the Connection Machine (1989)” ↗Consider browsing the criticism of Data Analysis with Open Source Tools(O'Reilly) for suggestions. Many people, including myself, expected a book that would have been more in line with what your book is probably trying to accomplish.
on “Python for Data Analysis (new O'Reilly book from creator of Pandas)” ↗