Fundamentals of programming for data science using R. Descriptive statistics, distributions and graphics in R. Relational database management systems including the relational model, relational algebra, database design principles and data manipulation using SQL. An introduction to the concept of big data.
5
UnitsOptional
Grading1, 2
PasstimeNone
Level LimitLetters and science
CollegeThe final was 50% of the grade and was quite difficult. If you keep up with the homework, worksheets, and lectures then the class isn't too difficult. The final was tough but she curved the grade. I personally wouldn't go to the professors office hours, go to the TA instead.
Cool prof, spoke to her during the quarter in OH/before class. Given my grade though, it wasn't her teaching style, but the 2 midterms+intimidating 50% final w/ detail-heavy questions which make up a majority of your grade that makes it tough (as others said). She curves but unless you want a challenging intro to R/data science, I'd look elsewhere
prof is a sweet lady. lectures are good, make sure to read through the slides and take notes beforehand. midterms were fine, very detail oriented. however, the final was insane. super detail oriented on things no one would study or know. make sure you study every single possible detail of codes, syntax, rules, and even the possible error codes
Holmes's attitude was rude and her questions extremely unfair, you quickly get the overwhelming sense she does not want you to succeed, worst professor I have had, for years students have been telling her that her questions are unfair but nothing changes
Midterms were chill but Final really caught me off guard..
final focused heavily on details and even content that wasn't explicitly taught in class. I spent what felt like millions of hours trying to memorize everything but the final still caught me off guard with obscure questions. lecture notes and slides have points presented misleading, and has errors with no correction. luck matters more than efforts