Introduction to Data Science with Python
A practical, golf-themed course by GolferHD — powered by AscenHD
Learn Python, pandas, data visualization, and strokes gained analysis using real golf data. Free, open-source, and beginner-friendly.
What You’ll Learn
This course teaches Python for data science through the lens of golf analytics. You’ll work with real golf data — shots, rounds, courses, and strokes gained — to learn the fundamentals of programming, data manipulation, and visualization.
Every topic follows a three-part structure:
- Concept — Understand the what and why
- Code — Implement it in Python
- AI — Do it with AI tools like Claude and ChatGPT, and learn to evaluate the output
By the end, you’ll have the skills to analyze your own golf game with Python.
Course Topics
| # | Topic | What You’ll Build |
|---|---|---|
| 01 | Getting Started | Python environment, first program, modules |
| 02 | Python Basics | Scoring functions, handicap calculator |
| 03 | Working with Files | CSV/JSON parsers, file I/O with golf data |
| 04 | Comprehensions & Generators | Data pipelines, filtering, transformations |
| 05 | Classes & Data Modeling | Golf domain model with dataclasses |
| 06 | Pandas & EDA | Exploratory data analysis on golf rounds |
| 07 | Data Visualization | Charts, dashboards, statistical plots |
| 08 | Intro to Strokes Gained | Personal performance report (capstone) |
Prerequisites
- No prior programming experience required
- A computer with internet access
- Curiosity about golf data (handicap not required)
Tech Stack
- Python 3.12+ with uv for environment management
- VS Code with the Jupyter extension
- Key libraries: pandas, matplotlib, seaborn, numpy
Get the Data
All course materials use a synthetic golf dataset with ~2,100 shots across 24 rounds at three real Pittsburgh courses: North Park, South Park, and Bob O’Connor. Download the data from the data/ directory.