CS 3501

The course topic that I took with Professor Miaomiao was Foundations of Data Analysis. Despite the name, the material in this course could be very challenging. Each lecture was recorded and attendance was not required but encouraged. The grading component of the course was sparse and consisted of homework assignments (70%) and exams (30%).

There were four homework assignments throughout the semester, and these were completed with Jupyter Notebook. For the homework, students were already expected to have a working knowledge of Pandas, NumPy, and data visualization libraries on Python. These homework assignments comprised conceptual and theoretical material and also programming. These could vary in difficulty, but there is a Piazza and TA office hours for guidance. There are many opportunities for extra credit (up to 30% on one of the homeworks!), which can massively boost your grades. Make sure you start on these as soon as possible.

The midterm exam was in-person, and I found it to be difficult but doable as there was a study guide provided on the topics we were expected to know. The final exam was take home and cumulative.

I believe this is a very useful class for those who want to go into data science/AI/machine learning, but sometimes there were things thrown at us in lecture that confused me. Professor Miaomiao does her best to help students and is very understanding. Although there are no enforced prerequisites, I highly recommend already having taken calculus, probability, and linear algebra before taking this topic. #tCFS24

Highly recommended if you are interested in AI or Statistics; 4 HW Set Problems in Jupyter Notebook; Lectures are recorded; Exams were not too difficult

- need to have math background (linear algebra; probability; calculus)
- not allowed to use AI but many similar solutions and tutorials can be found online (YouTube, GeeksforGeeks, etc)
- lectures were dry but very useful to hw assignments which consist the majority of your grade