I often encounter students and colleagues who have (sometimes extensive) backgrounds in computing in some language-MATLAB, IDL, R, Java, C++, etc.-and are looking for a brief but comprehensive tour of the Python language that respects their level of knowledge rather than starting from ground zero. To tap into the power of this data science ecosystem, however, first requires familiarity with the Python language itself. No less important are the numerous other tools and packages which accompany these: if there is a scientific or data analysis task you want to perform, chances are someone has written a package that will do it for you. For example, the manuscript for this report was composed entirely in Jupyter notebooks. IPython/Jupyter provides an enhanced terminal and an interactive notebook environment that is useful for exploratory analysis, as well as creation of interactive, executable documents.
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