Georgia Tech OMSA vs. University of Texas MS Data Science

Posted On May 9, 2021

Introduction

In a world of infinite options, choosing the appropriate graduate school to prepare you for a career as a data professional can be paralyzing. While graduate school is not the best option for everyone, it is a path that warrants consideration for many. Within the landscape of technical degrees that prepare you for the era of big data, these two masters programs have stood out amongst the many options. I have chosen to double click on these two programs for three primary reasons: School Ranking, Affordability, and Flexibility.

Reason #1: School Ranking

While school ranking is not the most important factor for choosing your school, it does provide assurance of a more rigorous program. Being challenged and learning the skill sets is incredibly important for having a long term career in tech. Less rigorous degrees will alienate you from the more technical positions and will not sufficiently prepare you for interviews. Technical positions often test your skill sets in interviews, especially, at the more desirable companies.

Higher ranked schools are more likely to have experts in their respective fields as professors and a stronger group of peers. The more intelligent and ambitious your classmates are, the more you will learn from the program. Depending on your source, both of these programs typically rank in the top 20 of computer science departments in the United States. U.S. News ranks Georgia Tech and the University of Texas as the 8th and 10th best computer science (CS) schools, respectively.

Reason #2: Affordability

The vast majority of graduate programs that prepare you to become a data professional are incredibly expensive, especially, if you focus on the higher ranked programs. KDnuggets, a popular source for tech material, published two fantastic visualizations that summarize the top programs by CS ranking and cost. The average tuition cost for these top programs is about 40,000 dollars. You will notice the Georgia Tech OMSA program stands out as one of the most affordable options with a price tag of 9,900 dollars*. Because the University of Texas MS Data Science program just started in early 2021, it does not appear on these visualizations. However, I would imagine they would be positioned near Georgia Tech on the graphs because their tuition cost is only 10,000 dollars*.

KDnuggest Viz #1: 2019 Best Masters in Data Science and Analytics – Online

KDnuggest Viz #2: Top Online Masters in Analytics, Business Analytics, Data Science – Updated

Reason #3: Flexibility

The wave of online education has made it much easier to learn anything as long as you have an internet connection. Both of these programs have online options. This provides flexibility for individuals to choose schools outside of their geographical region and makes it easier to adjust your schedule because you can work asynchronously.

Course Structure

Many data professional job postings will group technical degrees and skills together with reckless abandonment. Prospective students cannot afford (pun intended) to approach their decision process the same way. Diving into the two programs, they are preparing students for slightly different career path options.

As I have previously stated in another article, the Georgia Tech OMSA program is “interdisciplinary” and focused on breadth in the analytics space. The required courses cover a wide range of topics, including finance, accounting, object-oriented programming, data analysis, machine learning, statistics, web development, cloud computing, data cleaning, scripting languages, and data visualization. Their “specializations” are more for show than actual differentiation between themselves since they only account for 6/36 credits in the program. Because the program touches on so many topics, the opportunity cost becomes a lack of depth and specialization in any one particular topic. The main opportunity for specialization and depth comes with the final capstone project.

While the OMSA program emphasizes breadth in the analytics and data science space, the UT Data Science program is all about depth. The entire program is focused on machine learning, algorithms, statistics, data analysis, and visualization. They prepare individuals for analyzing, predicting, and interpreting data. These skill sets are useful to manipulate and draw insights from data after it has been engineered in an established system (i.e. data engineering, data product development, APIs, etc.).

There are several key differences between the two programs’ course structures:

• OMSA has a capstone project, while UT does not
• UT has an algorithms course that covers more fundamental computer science topics, while OMSA’s Introduction for Computing for Data Analytics course focuses more on manipulating data
• UT goes into more depth in building predictive models
• OMSA includes more data engineering subjects and courses (e.g. big data technologies, cloud, database models)
• OMSA includes more business courses as required classes

Career Paths

With more course offerings and specializations, the OMSA program offers a wider variety of career paths for a data professional. Because everyone comes in with different backgrounds, it is difficult to extrapolate which positions you are immediately qualified for upon graduation. However, if you augment your current skill sets with the OMSA program, it sets you on the right track to pursue most data professional careers. In my opinion, a student graduating with an OMSA degree and zero years of work experience is qualified for your typical analytics job posting looking for 3-5 years of experience. Examples include data analysts, senior data analysts, business intelligence engineers, junior data scientists, and data science associates. If you augment the degree with your own experience, then you could easily qualify for more higher level positions, such as a data scientist, analytics manager, or data product manager. However, most graduates will not qualify for the more specialized data science, artificial intelligence, or data engineering positions (e.g. applied scientists, machine learning researcher, data engineer, machine learning engineer). Overall, students who plan well can tailor the program slightly to have more of an emphasis on most data professional career paths.

With the UT Data Science program, they are preparing you for the more specialized data science career paths. Because of the heavier emphasis on predictive modeling, they are putting you on the right track towards any data science career that does not require a PHD. These careers will emphasize building, optimizing, and interpreting machine learning and statistical models. Some examples include senior analysts, data scientists, applied scientists, machine learning researchers, decision scientists, and statisticians. The program does not adequately prepare you for most careers with more of an emphasis on business/domain knowledge or data engineering (e.g. business intelligence engineer, analytics manager, data product manager, data engineer).

Other Factors

Obviously, the above discussion does not address everything on your pros vs. cons list between the two programs. Here are some additional questions to think about before making a decision:

• Does being around for a longer time give OMSA an edge because they have been able to improve over the years?
• Does the breadth of subjects make it harder to learn the material if you constantly switch between business, statistics, and programming in OMSA? This could be exacerbated by only taking one course per semester.
• Do you prefer more flexibility in career options (i.e. OMSA) or do you think that specialization (i.e. UT) gives you a better advantage in the hiring process?
• Would you prefer a stronger network tied to the Atlanta or Austin area?
• How important is it to dive deep into a specific problem via a capstone project?
• Which data professional careers will be in higher demand in 3-5 years?

Quick Warning

Before you jump into one of these two programs because of their low prices, you do need to consider the potential downside. Lower costs often means a higher student to teacher ratio. This means the quality of the instruction might be worse than programs that can afford to hire more resources. If the UT Data Science program is anything like my experience with OMSA, then the vast majority of your learning will be self-taught. You are primarily paying for the structure of a learning environment and a degree.

Recommendation

We cannot predict how the future of data science and analytics will unfold; however, it is likely that understanding how to manipulate and interpret data will be a useful skill for the foreseeable future. Both of these programs will open up doors to new opportunities as a data professional. If you have always had a knack for statistics, enjoy building predictive models, and dislike the “business-side” of analytics, then the UT Data Science program is likely the better option. However, if you prefer a better understanding of the broader landscape of analytics and data science and want more diversity in career options, then the OMSA program is the one for you. As a data professional, I am fully aware that I just made a ton of assumptions and extrapolated my n=1 experience to everyone else. My advice will not be perfect for everyone, but it is directionally correct for most. If you would like to have a more nuanced discussion about your career or education options, my door is open.

~ The Data Generalist™
Data Analytics Career Advisor

*Prices only include tuition