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http://nirvacana.com/thoughts/becoming-a-data-scientist/

This graphic is due to Swami Chandrasekaran.

I just started learning skills for data science recently. I have basics in R, Python, Statistics, Machine Learning. Now, I want to learn the Visualization and NLP part. What books should I begin with, that covers most of what listed above?

Practical books are preferred, because I want to try immediately with data available on internet (e.g. data.gov). Nevertheless, suggestions for theoretical books are welcomed too.

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http://www.amazon.com/ggplot2-Elegant-Graphics-Data-Analysis/dp/0387981403 http://www.amazon.com/Interactive-Data-Visualization-Scott-Murray/dp/1449339735/
and for the general philosophy of data visualisation and analysis of plots, let's go with immortal classic: http://www.amazon.com/Visual-Display-Quantitative-Information/dp/0961392142/ For NLP I'm curious about people's suggestions myself. In the meantime, I'm working my way through teaching materials from Dr Collins' website: http://www.cs.columbia.edu/~mcollins/ and they look great :)

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  • $\begingroup$ How do the second book related to R, Python or any other statistical software? Could you please explain it? I'm confused. $\endgroup$ – hans-t Mar 17 '14 at 15:17
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    $\begingroup$ Murray's book teaches you D3.js and JSON which are important tools especially for data scientists working in Web domain; the Roadmap you cite mentions those as well - in essence, it expands your toolbox. Tufte's work is actually older than R, Python and most modern statistical packages, it's more about the general approach and methods rather than using particular software. $\endgroup$ – Jacek Podlewski Mar 17 '14 at 15:26
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For a nice applied introduction to DV, I really like Nathan Yau's two books: Visualize This and Data Points. The first one is a bit more task oriented. For instance, it surveys the programming tools that are commonly used, including tutorials. There's some material on interactive graphics as well. The second is a bit more conceptual, though still with lots of examples.

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