This course on the mathematics of data has two intended audiences:
For math majors:
it is meant as an invitation to data science
from a mathematical perspective.
For (mathematically-inclined) students in data science (undergrad or grad):
it can serve as a mathematical companion to machine learning and statistics courses.
Content-wise it is a second course in linear algebra, vector calculus, and probability
motivated by and illustrated on data science applications. As such, students are expected
to be familiar with the basics of those mathematical areas, as well as
to have been exposed to proofs. Moreover, while the emphasis is on the mathematical concepts,
students enrolling in this class should be willing
to learn a programming language.
Current semester
Website for the current semester (including updated notes):
Julia: To install Julia and Jupyter notebooks, follow these
instructions.
Some resources for learning Julia:
A good place to start learning Julia is this
tutorial.
A more in-depth, but still quick, overview is this
video.
If you are already familiar with MATLAB or Python, this
cheat sheet is useful.
If you do not have much programming experience, this
textbook
may be helpful.
Python: Many students will already be familiar with Python. I recommend using
Google Colaboratory and
I will provide links to notebooks hosted there. Some resources for learning Python (mainly Numpy):