Includes index.

Preface -- I: Learning Python in a notebook environment -- 1. Introduction to notebooks -- 2. Fundamentals of Python -- 3. Sequences -- 4. Other data structures -- 5. Execution control -- 6. Functions -- II: Data science laboratories -- 7. NumPy -- 8. SciPy -- 9. Pandas -- 10. Visualization libraries -- 11. Machine learning libraries -- 12. Natural language toolkit -- III. Intermediate Python -- 13. Functional programming -- 14. Object-oriented programming -- 15. Other topics -- A. Answers to end-of-chapter questions.

Data science and machine learning two of the worlds hottest fields are attracting talent from a wide variety of technical, business, and liberal arts disciplines. Python, the worlds #1 programming language, is also the most popular language for data science and machine learning. This is the first guide specifically designed to help millions of people with widely diverse backgrounds learn Python so they can use it for data science and machine learning. Leading data science instructor and practitioner Kennedy Behrman first walks through the process of learning to code for the first time with Python and Jupyter notebook, then introduces key libraries every Python data science programmer needs to master. Once youve learned these foundations, Behrman introduces intermediate and applied Python techniques for real-world problem-solving. Throughout, Foundational Python for Data Science presents hands-on exercises, learning assessments, case studies, and more all created with colab (jupyter compatible) notebooks, so you can execute all coding examples interactively without installing or configuring any software.