Course Number: Data 8

Course Name: Foundations of Data Science

Units: 4

When is it offered? FALL/SPRING

Requirement Satisfied: None

Concentration(s): N/A

Summary: This class is an introduction to thinking like a data scientist, as well as using Python. The entire class is implemented online through Jupyter Notebook and the syntax/language used in the class is nicer therefore than "real" python. You also learn about statistical methods and how to apply them with python.

Official Prerequisites: None

LEGIT Prerequisites: None, but programming experience makes it much easier. Then again, if you don't have programming experience this is a good class to start with.

Topics Covered: 1. Tables 2. Data types 3.Histograms 4. Functions 5.Chance/Sampling 6.Comparing Distributions 7. A/B testing 8.Distributions 8.Linear regression 9. Least Squares 10. Residuals 11. Regression Inference 12.Classification

Workload: Not very high compared to most classes. One lab and one homework per week. 3 projects throughout the year (partner), 1 midterm and 1 final. It is good to start early on these so that you don't have to cram last second, and additionally you get an extra credit point for submitting one day early (for homeworks). Lectures are webcasted, so don't fall for the trap of forgetting to watch them and then wind up being 10 lectures behind.

When to take? Whenever

"Whats next" Courses? Data 100

Usefulness for research / internships: Good for internships as you can add Python to resume as well as data science skills

Added Comments or Tips: Enjoyable class with low workload and many resources. 45+% A's and 35+% Bs. Highly recommend this class for people without extensive coding experience.

AIChE Berkeley