Although every data science project is different, for our illustrative purposes, we can partition them into a series of reduced and simplified phases.

The process starts with the obtaining of data, and this implies a series of possibilities, from simply uploading the data to assembling it from RDBMS or NoSQL repositories, and to synthetically generating it or scraping it from the web APIs or HTML pages.

Though this is a critical part of the data scientist's work, especially when faced with novel challenges, we will just briefly touch upon this aspect by offering the basic tools to get your data (even if it is too big) into your computer memory by using either a textual file present on your hard disk or the Web or using tables in RDBMS.

Then comes the data munging phase. Data will be inevitably always received in a form unsuitable for your analysis and experimentation. Thanks to a bunch of basic Python data structures and commands, you'll have to address all the problematic...