Expand beyond Computer Science into Statistics, Probability and Data Science
Note: The data science specializations are more involved and will require more effort than some of the other specializations.
| Courses | Status | Evidence |
|---|---|---|
| Data Science | ||
| M001: MongoDB Basics | ||
| Applied Data Science with Python | ||
| Deep Learning | ||
| M220P | ||
| Take this: Introduction to Probability and Statistics (more rigorous) | ||
| or this: Khan Academy Probability and Statistics (a more gentle introduction) | ||
| Reading | Status | Evidence |
| Think Python | ||
| Pandas Docs | ||
| Think Stats | ||
| Numpy Docs | ||
| An Introduction to Statistical Learning | ||
| Think Bayes | ||
| Practice | Status | Evidence |
| Do 10 problems (of your choice) on Rosalind | ||
| Complete one competition of your choice from Crowd Analytix | ||
| Complete one Bot Programming Competition on CodinGame | ||
| Complete Deep Learning - TensorFlow on CodinGame | ||
| Do 20 problems (of your choice) on Rosalind | ||
| Complete the Digit Recognizer competition on Kaggle | ||
| Complete the Hackerrank Probability Challenges | ||
| Complete the Hackerrank Linear Algebra Foundations Challenges | ||
| Complete one competition of your choice from Crowd Analytix | ||
| Complete one competition of your choice from Analytics Vidhya | ||
| Complete one competition of your choice from Driven Data | ||
| Complete one competition of your choice on Kaggle | ||
| Capstone | Status | Evidence |
| Create a website highlighting what you learned and built during this tier. Use this as an opportunity to create a portfolio of your projects, notes, blog posts, etc. |