Data science and machine learning―two of the world’s hottest fields―are attracting talent from a wide variety of technical, business, and liberal arts disciplines. Python, the world's #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 you've 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.
I: Learning Python in a Notebook Environment 1
1 Introduction to Notebooks 3 2 Fundamentals of Python 13 3 Sequences 25 4 Other Data Structures 37 5 Execution Control 55 6 Functions 67
II: Data Science Libraries 83
7 NumPy 85 8 SciPy 103 9 Pandas 113 10 Visualization Libraries 135 11 Machine Learning Libraries 153 12 Natural Language Toolkit 159
III: Intermediate Python 171
13 Functional Programming 173 14 Object-Oriented Programming 187 15 Other Topics 201 A Answers to End-of-Chapter Questions 215
About the Author
Kennedy Behrman is a veteran software and data engineer. He first used Python writing asset management systems in the Visual Effects industry. He then moved into the startup world, using Python at startups using machine learning to characterize videos and predict the social media power of athletes.