Python. Pandas. Web Scraping. Databases. SQL. Machine Learning. APIs.
All applied to Baseball Statistics
Learning to code isn't hard, there's just a learning curve. That's why the most important thing is starting with a project you're excited about.
This book will take you from playing around with stats in Excel to scraping websites, building databases and running your own machine learning models.
“This book was really, really well done.”
You'll learn — step by step and applied to baseball — how to program your own analysis. You'll also learn how to make plots like these 👇:
“Amazingly awesome... the way the learning is framed here is 10x what you'll get someplace else.”
About LTCWBB and the football version, which this book is based off of —
“I was amazed by how you broke down complicated concepts and made them easier to understand.”
Get Learn to Code with Baseball
Includes book, datasets, example scripts, end of chapter problems with full solutions, and flashcards.
The 270 page book in PDF format
300+ spaced repetition flash cards
23 example scripts, 10 datasets
100+ practice problems with full solutions
30 day money back guarantee!
30 Day Money Back Guarantee
Try it! If you're not satisified, contact me within 30 days and I'll refund you the purchase price.
“I've taken automate the boring stuff, python for finance, etc and while those course are great... I seem to be understanding it better because its about a subject I like.”
See the full table of contents
Python — This flexible language is the foundation of everything from data munging to web scraping to machine learning. You'll also learn about its key data library Pandas, the modeling and machine learning libraries statsmodels and scikit-learn, and how to do data visualizations with seaborn.
Web Scraping and APIs — Next time you run across a site with data you'd like to analyze you'll know how to grab data via its public API if it's available, or build a web scraper to get it yourself if it's not.
Machine Learning and Statistics — You'll learn the difference between a regression and a random forest, and will know when and how to build both.
Databases and SQL — Build your own database — whether it's for player statistics, to keep track of opponent tenancies, etc — and use SQL to get data in and out of it.
All in the context of baseball and designed so you can learn how to apply them to your own questions and do your own analysis.
Hi! My name is Nate and I'm a self-taught programmer and data scientist based in Milwaukee, WI.
A few years ago, I didn't know anything about Python, SQL, machine learning, web scraping or any of the other topics covered here.
So, I taught myself. It took a few years and I ran into a lot of dead ends along the way, but ultimately I figured it out. In this book, I distill everything I've learned to provide a step-by-step guide to doing baseball analytics and get you up and running as quickly as possible.