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. |