Minor data science
Minor: Data Science
By Ryan Burruss
Period 1 (1 of last 2 options)
Intro to Data Science (required)
Data Structures & Algorithms
Strategic Management of Technology & Information
Period 2 (2 out of 3 options)
Data Analytics & Privacy
Data Wrangling (required)
It might sound strange that an Econometrics & Data Science student would want to take a data science minor outside of his own department, especially considering two of the courses on-offer had literally the same title as mandatory classes in the program, but given the discrepancy between the thoroughly abstract/theoretical major and the job-oriented/practical minor, it was actually quite a reasonable choice.
First, regarding the two courses bearing the same name, Data Structure & Algorithms was indeed so similar that the minor’s administrators wouldn’t allow students from our program to take that class (removing the freedom of choice for P1). Thus, though this course might be of some value to the EOR students for whom it isn’t required, I can’t speak on it from experience (the same goes for Data Analytics & Privacy, which I didn’t elect to take).
As for Intro to Data Science, while the minor version of the course actually functioned as a proper introduction to the field, the EDS version is nothing more than a rebranded version of EOR’s course Probability Theory, and thus the two variants had little overlap. In the minor version, we touched on a wide variety of practical applications of machine learning algorithms from a practical perspective, which offered yet another important contrast, but this time to EDS’s Year 3 Machine Learning course (which instead centers around the theory behind ML). In a similar vein, the Logistics Analysis course functions as a sort of practical version of Operations Research, and while a number of the Computer Science and Business Analytics students in the class found it to be the hardest part of the minor, those of us who had taken OR courses found it quite manageable.
As for the remaining courses, Strategic Management of Technology & Information was a much more managerial-focused course, and thus felt similar to Macroeconomics with respect to its balance of reading versus math needed (surprisingly, the exam was quite difficult for such a non-theoretical course). Finally, both Information Retrieval and Data Wrangling, like Intro to Data Science before them, were very coding-heavy, practically-oriented courses centering around individual and team-based projects, respectively.
All told, I found this minor a breath of fresh air after two full years of the highly abstract/theoretical courses in the Econometrics department, and I fully recommend it to anyone looking to either fill in the practical gaps in our programs or better prepare themselves for going into the workforce directly after their Bachelor’s in place of pursuing the full research-oriented degree cycle.