When one of my favorite independent tech book publishers, No Starch Press, notified me about their new title “Doing Math with Python,” I was energized to review what potentially could be a good new resource for budding data scientists. I wasn’t disappointed. The book contains all the important first ingredients that a newbie data scientist needs to get started in the field. I particularly like the chapters on data visualization, statistics, and probability, not to mention the chapter on solving Calculus problems such as finding derivatives of functions, a topic that surfaces in machine learning methods like gradient descent.
The book begins with simple tasks like visualizing data with graphs and writing programs that convert units of measurement, and ramps up to cover more advanced topics like drawing fractals and finding the limit of functions. An early stepping stone for those just starting out in the field of data science, the book will teach readers how to:
- Describe data with statistics, and visualize it with line graphs, bar charts, and scatter plots
- Explore set theory and games of chance using probability
- Solve algebraic problems automatically using Python’s symbolic math functions
- Draw geometric shapes and explore famous fractals like the Barnsley fern, the Sierpinski triangle, and the Madelbrot set
- Write programs to find derivatives and integrate functions
Challenges and applied examples throughout the book show readers how to put their new math and coding skills into practice. The book is a great way to brush up on your math and programming in pursuit of using machine learning techniques for the solution of data science problems. You have to start somewhere, and if Python is your data science language of choice, then this book is a great way to start.
The author is Amit Saha, a software engineer who has worked for Red Hat and Sun Microsystems. He created and maintains Fedora Scientific, a Linux distribution for scientific and educational users.