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.;Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. You'll get familiar with the Python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide. It covers the fundamentals of Python data analysis programming and statistics to help you build a solid foundation in data science concepts like probability, random distributions, hypothesis testing, and regression models; explains objects, functions, modules, and libraries and their role in data analysis; walks you through some of the most widely-used libraries, including NumPy, SciPy, BeautifulSoup, Pandas, and MatPlobLib. --