pt. I. Getting started with Python for data science -- Discovering the match between data science and Python -- Introducing Python's capabilities and wonders -- Setting up Python for data science -- Reviewing basic Python -- pt. II. Getting your hands dirty with data -- Working with real data -- Conditioning your data -- Shaping data -- Putting what you know in action -- pt. III. Visualizing the invisible -- Getting a crash course in MatPlotLib -- Visualizing the data -- Understanding the tools -- pt. IV. Wrangling data -- Stretching Python's capabilities -- Exploring data analysis -- Reducing dimensionality -- Clustering -- Detecting outliers in data -- pt. V. Learning from data -- Exploring four simple and effective algorithms -- Performing cross-validation, selection, and optimization -- Increasing complexity with linear and nonlinear tricks -- Understanding the power of the many -- pt. VI. The part of tens -- Ten essential data science resource collections -- Ten data challenges you should take.