SDS 4030
Statistics for Data Science II
This builds on the foundation from the first course (SDS I) and further develops the theory of statistical hypotheses testing. It also covers advanced computer intensive statistical methods, such as the Bootstrap, that will make extensive use of R. The emphasis of the course is to expose students to modern statistical modeling tools beyond linear models that allow for flexible and tractable interaction among response variables and covariates/feature sets. Statistical modeling and analysis of real datasets is a key component of the course. Prerequisites: Math/SDS 3211, or Math/SDS 3200 and Math/SDS 493; Math/SDS 439 (Math/SDS 439 can be taken concurrently).
Instructors
Reviews
We never got our midterm projects (from March) OR our final project back. We never even got to see what score we got on them. I'm pretty sure she never graded them and then just gave everybody an A in the class because the deadline for submitting grades was approaching fast. If I wanted to learn with no feedback I would go to YouTube. 0/10
5/28/2024
The homework in this class is weekly with tons of math and programming that take 6-10 hours to complete. This class is tedious and hard, but it does an amazing job of teaching you useful and practical math and computer science methods to solve real-world data science problems. Biggest issue is the lack of feedback -- I got no comments on anything.
4/30/2024
Professor Ding is awesome! Her class is hard, don't get me wrong, but she gives you the best tools to succeed that she can. Exams are only 30% of her class and she allows a one-sided cheat sheet and gives you a study guide. It is a difficult class and you have to put in the work, but Professor Ding gives great lectures and grades with kindness.
3/5/2024