Overview and use of data science tools in R and/or Python for data retrieval, analysis, visualization, reproducible research, and automated report generation. Case studies will illustrate the practical use of these tools.
4
UnitsOptional
Grading1, 2, 3
PasstimeNone
Level LimitLetters and science
CollegeNice person but not very accessible outside of class. Lectures are lackluster, expect to self study a big portion of the class. Dont expect to leave with good knowledge of mathematical statistics.
I just self-studied for all the materials and got an A. Exams become easy if you can 100% solve all these practice problems.
Very bad and boring lectures. Do not take this professor if you actually want to learn anything.
The instructor was largely unresponsive to emails, including inquiries about the final exam. Even the TA had difficulty reaching her. Lectures were minimal and lacked depth, requiring significant self-study to truly understand the material. If you're looking for a course with strong guidance and engagement, this might not be the best choice.
Good professor with knowledge of the subject. Straightforward in their expectations and lectures.
The lectures were lackluster and behind schedule, making the course feel a lot harder than it really was. Coburn grades generously, but I feel that I've learned very little in her class.