After writing my first review of the OMSA program, I have been contacted by many prospective and current students. Because this program attracts students from a wide variety of backgrounds, providing advice that applies to everyone can be challenging. Below are some of the most common questions I receive and my attempt to answer them.

Is OMSA the right program for me?

If you read the program description on their degree overview page, they use the descriptor, “interdisciplinary”. I think this was the perfect word choice for the program. The range of topics covered in this program come from a wide variety of disciplines. The required courses cover finance, accounting, object-oriented programming, data analysis, machine learning, statistics, web development, cloud computing, data cleaning, scripting languages, and data visualization. The emphasis of this highly technical program is breadth over depth. If that sounds appealing, then this might be the program for you. However, if you wish to become an expert and specialize in any one of those individual disciplines, there are better options than this program.

Although this program emphasizes breadth, do not underestimate the difficulty level of the material. Being competent across business, computer science, and statistics is no easy feat. Often individuals will struggle with at least one of the disciplines covered in this program. If you are concerned with the technical rigor of the degree, I would advise you to improve your skills prior to enrollment or consider other programs. Other online quantitative master’s degrees include Data Science, Applied Economics, Statistics, Computer Science, Business Analytics, and an MBA: Analytics Concentration. Analytics programs run out of a university’s business school will likely be less rigorous in mathematics and computer science.

Which OMSA specialization should I choose?

After you finish the required courses, there are 3 specializations you can choose from. The word, specialization, is a bit misleading because the majority of the program is the same for every student. Only 6/36 credits are tailored to that specialization; otherwise, the rest is required or up to your discretion. You can quickly summarize the three specializations with the following:

Although the difficulty level is somewhat subjective, I think most students would agree with my assessment. The computer science courses tend to require the most hours of work per week.

Is there a particular order of classes that you would recommend?

With so many different disciplines covered in this program, it can be tricky to decide the appropriate order of classes to take. While deciding, there are many variables to take into account. Here are just a few of those questions to answer before you create your ideal 2 to 4-year graduate plan.

How many classes can you take per semester?
The average workload ranges from 8–20 hours per week per 3 credits, depending on the course. You need to consider which classes can be paired together based on the class expectations. Your individual time commitment will differ from the average depending on the strength of your skills in that subject. For example, if you have a strong background in statistics, then you can likely assume a below-average time commitment for statistics courses.

Which classes are offered each semester?
Summer semesters are shorter than Fall and Spring. Therefore, they only offer courses that can move at a quicker pace, which is a subset of the full class list. Because they run at a faster pace, the average workload per week is about 20 percent higher than average.

Which classes are optimal to take in close proximity?
It is much easier to take two related disciplines in close proximity to each other. Personally, I was at the peak of my programming skills after finishing Introduction for Computing for Data Analytics. I wish I took the hardest required computer science course, Data and Visual Analytics, immediately after this one. You should consider which classes pair well together as complementary subjects.

Is the OMSA program worth it?

To determine worth, you need to estimate the cost of the program and the expected return of completing it. The monetary cost for this program is 13,000 dollars*, but the labor cost is 2,160 hours** to complete the program. In terms of comparable graduate degrees, the tuition of this program is towards the lowest end of the spectrum. However, if you think the value of credentialism is waning in society, there are much cheaper options to learn this material (e.g. Coursera, Free online resources, etc.). If the cost of tuition doesn’t concern you the time commitment should. As stated previously, this program is about breadth in the analytics space rather than depth. If you wish to become an expert at a subset of analytics, such as experimentation, artificial intelligence, or data engineering, then your time is better spent elsewhere.

From a return perspective, the program primarily offers two things: a structured learning environment and, at a minimum, an entry-level data analytics position. The program offers a well-thought-out syllabus for most courses from experts in each of their respective domains. The assignments are organized and typically have auto-graders whenever possible for timely feedback. The environment includes multiple modes of communication to find assistance on assignments, including Piazza, Slack, and office hours sessions. Reading through dozens of posts on Piazza to find a hint at solving a particular problem can be a bit of a mess. This is about the extent to which they enable your learning. You will notice I did not mention lectures anywhere above. They can be hit or miss, depending on the course. Many lectures are so high level that you would be wiser to skip them entirely. Some lectures or assignments will include helpful links; however, the majority of your learning takes place outside of the GT environment through your own research (AKA Googling). As far as a return perspective, the knowledge you gain from this program is highly dependent upon the effort you put into learning the material versus simply getting a degree.

Moving onto the primary reason most people enroll in a graduate program, job opportunities. I don’t need to reiterate that data analytics skills are in hot demand. With increasing reliance on technology in every industry, I don’t see any future where computer science and statistics skills will not be marketable. Artificial intelligence will only increase our reliance on these skills because each automated process requires the installation and ongoing maintenance. Because there are so many types of individuals enrolled in this program, it is hard to say how much this degree will help you land the job you desire. 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. However, if you want one of these positions at a top company or a more senior analytics role, you need to augment the degree with additional work experience or personal projects.

It’s up to you to decide whether adding the equivalent of an extra 3–5 years of data analytics experience is worth the cost of this program. I think the program is most valuable for those who fit at least one of the following criteria:

• An experienced professional with some quantitative/programming background looking to switch careers into data analytics
• An experienced analytics professional who wants to accelerate their professional advancement by 3–5 years
• A student who needs structure to facilitate their learning
• A student who is not afraid of complex problems that require persistence
• An experienced professional who does not want to specialize in a specific data analytics or data science subject (e.g. Artificial Intelligence, Machine Learning Engineering, Data Engineering, Data Visualization, etc.)

*(275 per credit hr * 36 credit hours) + ((194 + 107 fees per semester) * 9 semesters)
**(12 hours per week per 3 credits * 15 weeks per semester) * (36/3 three credit blocks)

How can I prepare for this program?

If you decide to apply and enroll in this program, you need to confirm you are prepared for the material. The admission requirements are suboptimal in preparing students for this program. In order to be successful in this program, I would recommend having a certain level of experience across data analytics, mathematics, and computer science.

Data analytics topics and tools you should be familiar with:

• SQL
• Excel
• Data visualization
• Data cleaning
• Data analysis

In terms of math, you should be comfortable with the following:

• Calculus- integrals, derivatives, functions, limits
• Statistics — hypothesis testing, p-values, confidence intervals, sampling
• Probability- distributions, error
• Linear Algebra- matrices, matrix operations, vectors, systems of equations

As far as computer science, you should have completed the equivalent of two, rigorous undergraduate computer science courses prior to this program. Rigorous means they required 10+ hours per week per 3 credits. You need to be an intermediate level programmer in at least one object-oriented programming language. This includes experience with debugging and diagnosing issues within code. Familiarity with web development will help, too. Most of all, you need an insatiable appetite for tackling difficult problems that will rarely be correct on the first attempt.

If you have any additional questions or comments, feel free to reach out. I am ALWAYS thinking about data analytics or finance.

~ The Data Generalist