Or you could clone his Github repo, and do it in Jupyter:

https://github.com/wesm/pydata-book

I was really pleased to notice that the second edition of the "Pandas book" (https://www.amazon.com/Python-Data-Analysis-Wrangling-IPytho...) just came out in late October. I'm about halfway through reading it now.

Data Science from Scratch http://amzn.to/2dD9Iba

Python for Data Analysis http://amzn.to/2dDw6fL

Web Scraping with Python: Collecting Data from the Modern Web http://amzn.to/2eov4dZ

Python Machine Learning http://amzn.to/2eobdt3

http://sebastianraschka.com/books.html

There's a large number of such books, though none that are as authoritative with respect to Python (this is a statement about the size of Python's community vs. R, not necessarily about the authors):

- via Wes McKinney, creator of pandas (which makes Python about as close to R as you can get): https://www.amazon.com/Python-Data-Analysis-Wrangling-IPytho...

- http://joelgrus.com/2015/04/26/data-science-from-scratch-fir...

There are a bunch of books specific to machine learning too though I haven't read them myself.

Good article for beginners. A couple thoughts, just to build on what the author said:

First off, data science == fancy name for data mining/analysis. Wanted to clear that up due to buzzwordy nature of "data science."

Learn SQL - this is the big one. You must be proficient with SQL to be effective at data science. Whether it's running on an RDBMS or translating to map/reduce (Hive) or DAG (Spark), SQL is invaluable. If you don't know what those acronyms mean yet, don't worry. Just learn SQL.

Learn to communicate insights - I would add here to try some UI techniques. Highcharts, d3.js, these are good libraries for telling your data story. You can also do a ton just with Excel and not need to write any code beyond what you wrote for the mining portion (usually SQL).

I would also go back to basics with regards to statistical techniques. Start with your simple Z Score, this is such an important tool in your data science toolbox. If you're just looking at raw numbers, try to Z-normalize the data and see what happens. You'd be surprised what you can achieve with a high school statistics textbook, Postgres/MySQL (or even Excel!), and a moderate-sized data set. These are powerful enough to answer the majority of your questions, and when they fail then move on to more sexy algorithms.

Edit: one more thing I forgot to mention. After SQL, learn Python. There are a ton of libraries in the python ecosystem that are perfect for data science (numpy, scipy, scikit-learn, etc). It's also one of the top languages used in academic settings. My preferred data science workspace involves Python, IPython Notebook, and Pandas (This book is quite good: http://www.amazon.com/Python-Data-Analysis-Wrangling-IPython...)

I guess the question is what do you mean by advanced topics? What direction do you want to go in? The latter book you mentioned seems to cover a number of topics and is probably a good bet.

If you are interested in the web, both these books were good: http://www.amazon.com/Python-Web-Programming-Steve-Holden/dp... http://www.amazon.com/The-Definitive-Guide-Django-Developmen...

Here are a few books that cover some "advanced?" topics that I'd like to read when I have time (would also like to hear other peoples' recommendations on them): http://www.amazon.com/Python-Data-Analysis-Wes-McKinney/dp/1... http://www.amazon.com/Twisted-Network-Programming-Essentials... http://www.amazon.com/Foundations-Python-Network-Programming... http://www.amazon.com/Introduction-Tornado-Michael-Dory/dp/1... http://onlinebookplace.com/programming-computer-vision-with-...

I'm not sure on your background or the quality of these books, but an understanding of data structures, algorithms, and object oriented programming could be considered important: http://www.amazon.com/Data-Structures-Algorithms-Using-Pytho... http://www.amazon.com/Python-Algorithms-Mastering-Language-E... http://www.amazon.com/Python-3-Object-Oriented-Programming/d...

Although these and other intermediate to advanced topics tend to be covered better in non-language-specific books such as this shotgun blast to the head. Don't worry, it's just an "introduction": http://www.amazon.com/Introduction-Algorithms-Thomas-H-Corme...