My personal path to a self-taught education in Data Science!
| Courses | Duration | Effort |
|---|---|---|
| MIT OCW Mathematics for Computer Science |
| Courses | Duration | Effort |
|---|---|---|
| Linear Algebra - Foundations to Frontiers | 15 weeks | 8 hours/week |
| Applications of Linear Algebra Part 1 | 5 weeks | 4 hours/week |
| Applications of Linear Algebra Part 2 | 4 weeks | 5 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Introduction to Probability | 16 weeks | 12 hours/week |
| Foundations of Data Analysis - Part 1: Statistics Using R | 6 weeks | 3-6 hours/week |
| Foundations of Data Analysis - Part 2: Inferential Statistics | 6 weeks | 3-6 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Python for Data Analysis, 2nd Edition |
| Courses | Duration | Effort |
|---|---|---|
| Introduction to Data Science | 8 weeks | 10-12 hours/week |
| Data Science - CS109 from Harvard | 12 weeks | 5-6 hours/week |
| The Analytics Edge | 12 weeks | 10-15 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Statistical Learning | 9 weeks | 5 hours/week |
| Stanford's Machine Learning Course | 11 weeks | 8-12 hours/week |
| Hands-On Machine Learning with Scikit-Learn and TensorFlow |
| Courses | Duration | Effort |
|---|---|---|
| Deep Learning Book | ||
| Creative Tensorflow | 60 hours |
- Intro
- My undergraduate CS program
- Data Wrangling
- Big Data / Spark
- Projects / Kaggle
- Specializations
- Blog
- Software Engineering
- Back End development
- Additional Resources