In fall 2021, I started Georgia Tech’s Online Master’s of Science in Analytics (OMSA). Here are some thoughts so far on the courses I’ve taken and overall experience as I head into my sixth class of the program.
OMSA Program Background
Georgia Tech’s OMSA program is one of a few well-known online graduate programs in the data community. As data science and analytics become more mainstream and academia leans further into online curriculums, I assume similar program offerings will continue to grow. I heard about Georgia Tech’s Master’s in Analytics initially through my brother who knew about their online cybersecurity program. My main deciding factors were the online format and price tag of about $10,000 USD. I completely support building data skills through free MOOCs (shoutout to Codecademy and O’Reilly, both of which I still find as great resources). However, I figured the structure, schedule, and breadth of what Georgia Tech offered would keep me accountable in my studies. There are 3 different tracks for coursework: Analytical Tools, Business Analytics, and Computational Data Analytics. I am in the Analytical Tools track.
CSE 6040: Computing for Data Analysis – Fall Semester 2021
This class works primarily in Python to illustrate computing concepts. The homework assignments and tests were auto-graded from JuPyter notebooks and were open book and open internet. The instant feedback was helpful but overall I found the time restrictions for the midterm and final to be challenging. I found creating a comprehensive Python file with code snippets from the whole class helpful for quick searches on the final. There were some optional course items like a project that could be submitted for extra credit. Aside from some difficulties with overthinking, the class was fairly enjoyable for the range of information it covered.
ISYE 6501: Introduction to Analytics Modeling – Fall Semester 2021
ISYE 6501 is essentially a whirlwind tour of analytics modeling concepts through R. Homework assignments are peer-graded and quizzes allow for a restricted number of note sheets (1 or 2 pages). This was a solid refresher in R and I found the content helpful for other classes like Regression Analysis. The course provided a decent background to the theory and troubleshooting involved in real world analytics problems.
MGT 8803: Business Fundamentals for Analytics – Spring Semester 2022
The class covered as many business concepts as possible including marketing, accounting, and supply chain optimization. There were different professors who helped guide the modules so it was interesting to have that mix for course. The level of straight memorization required to succeed with graded assignments was a bit much for me. Unfortunately this was a required course so whether or not the evaluation style was in my lane, I had to deal with it.
ISYE 6414: Regression Analysis – Summer Semester 2022
Regression Analysis gave a breadth of model building and illustrated underlying concepts behind each. The course uses R and material included cleaning and transforming data, variable selection, and linear and logistic regression. The homework assignments were fairly simple and I found having code snippets prepared in one file for the open book portion of exams to be helpful.
CSE 6242: Data and Visual Analytics – Fall Semester 2022
As one of the advanced requirements for the OMSA program, I was a little hesitant on the learning curve this course was rumored to have. The class was a grand tour of dabbling in different languages and programs like Python, SQL, Spark, and D3. A course project is a huge component of this class and it was nice to collaborate with classmates and translate what we learned into something tangible. Overall the homework assignments were only a major bummer because you could have a solution that looked exactly like the answer, but still get zero or minimal points from the auto-grader. There are no explicit homework solutions and I struggled with understanding what exactly needed to be corrected when I did not get full credit. Luckily, there were ample opportunities for extra credit to make up for any missed points from the homework.
ISYE 7406: Data Mining and Statistical Learning – Spring Semester 2023
I’m currently a couple weeks into the course for the Spring 2023 semester but so far the blend of theoretical background for statistics and practical R analysis has been manageable.
Balancing Professional, Academic, and Social Obligations
I attended a meetup in December 2022 at the main Atlanta campus for the Analytics and Cybersecurity programs and reflected on my personal experience after speaking with fellow students and alumni. There seemed to be a decent mix of professional backgrounds and mostly everyone I spoke with also worked full time for the duration of the program.
Personally, I have found that one course a semester has worked best for me. The only semester I doubled up was my first semester for two of the required core classes: ISYE 6501 and CSE 6040. In hindsight I think I would have been better off just taking one course instead of constantly feeling like I was flipping back and forth between the two.
For time spent on homework and studying, I have found that chipping away a little bit everyday has given me the best results thus far. It tends to keep concepts relevant as opposed to taking couple-day breaks between learning material. There have still been times where I have taken breaks for travel or vacation but a lot of the courses allow for some leniency for frontloading assignments and learning with the schedules for module releases.
Overall OMSA Experience
I’ve been pleasantly surprised in the program so far but still have plenty of days where I have the same frustrations that anyone else might with learning new material. The practical translation of concepts from the courses seems to be the main draw and highlight for folks in the OMSA program. I look forward to continuing to build skills in future classes. I am likely taking MGT 6203 (Data Analytics in Business) for summer 2023 and then ISYE 6669 (Deterministic Optimization) for fall 2023.