machine learning engineer with experience working at big tech companies like Meta, Google and LinkedIn. Even with a job at a top tech company and the chance to work on genuinely interesting machine learning problems, I decided to pursue a part-time online master’s degree at the University of Texas at Austin, and I recently completed it. In this post, I share my thoughts on whether an online master’s degree in AI, Data Science or CS can be worth it for building a career in this field.
Online Master’s Degrees For AI
Over the last few years, several top universities have been offering online master’s degrees in computer science, AI and data science. Some notable ones from renowned universities are:
These programs are exactly the same as their in-person counterparts. Students take the same courses, learn from the same professors, complete the same assignments, and receive the same diploma at the end (at UT Austin for instance, online master’s students are also invited to attend the same graduation ceremony alongside on-campus students).
The only difference is the mode of delivery. The lectures are recorded and watched online, all interactions between professors, TAs, and students happen online (via Zoom, internal forums or chat rooms), all the assignments and exams are completed and uploaded online.
Are They Worth It?
For people already working in the industry, I think they are, provided you have the right reasons for doing one and choose a good program. In the rest of the post, I explain my reasoning backed by real data and my own personal experience.
Benefits
Curriculum Encourages Completion (and thus, learning)
Even though most of the knowledge on data science, machine learning and AI is freely available on the internet today, a structured degree program pushes you to actually work through it and see it through to completion.
Let us look at some real data. The completion rate of such actual degree programs is significantly higher than MOOCs, as evidenced by the following chart, which shows data from various programs of different delivery modes — MOOC, online degree, and campus degree. In this data, online degree programs tend to have a significantly higher completion rate than MOOCs. On-campus programs of course have the highest completion rates, but they come with significantly higher costs compared to online programs (more on that further below in this article).
Image generated by author. Specific completion rate numbers taken from This was CS50X by David J. Malan; Massive open online course completion rates revisited: Assessment, length and attrition by Jordan, 2015; MOOCs completion rates and possible methods to improve retention-A literature review by Khalil & Ebner, 2014; Five Years of Graduate CS Education Online and at Scale by Joyner & Isbell, 2019
From my own personal experience: prior to starting the degree, I had bookmarked dozens of YouTube playlists, bought books and MOOC courses on Reinforcement Learning and Advanced Linear Algebra. Yet, I had never gotten to finishing all of them. The list only kept growing. But when I enrolled in the master’s program, I was pushed to actually complete courses I signed up for. For example, as part of the RL course, I was compelled to read the gold standard book on Reinforcement Learning by Barto and Sutton in its entirety, take notes, complete assignments, and prepare for exams. I can say with certainty that if not for the program, I would have never studied the subject to that depth.
As I finished courses in the program, I could start to see positive impact on my work as an ML engineer. For example, I got to apply contextual bandits (which I learned from the Reinforcement Learning course) for personalizing ad ranking treatments to users, and quantization and low-rank adapters in deep learning (from the Advances in DL course) in large models. Furthermore, my learnings helped me engage and participate in discussions with my teammates at work, review their work, and help support my role as a technical leader. Some of these projects and activities might have felt too intimidating without the strong foundational knowledge I got from the courses.
But to set expectations correctly, I have generally found that the machine learning problems at work are theoretically simpler and solvable with simpler techniques compared to the complex and diverse concepts I studied as part of the degree program. However problems in the industry are incredibly complex to productionize and launch at scale, which only industry experience can prepare you for.
They Offer A Rich Set Of AI Courses
Most of the good programs I have looked at offer a wide variety of modules related to AI and Data Science. It is really refreshing to see that some of the module material was based on papers published as recently as a year or two ago, showing that universities are trying to keep the material relevant.
In no particular order, here is a sample of various modules I have looked at from various universities. I have verified that their online MS programs offer each of these modules.
Mathematics courses
Mathematics is the bedrock of artificial intelligence. Mastering linear algebra, probability, and calculus is essential for deconstructing the inner workings of models and interpreting complex research papers with confidence.
Statistics courses
Statistical rigor is essential for both industry and academia for running robust experiments, navigating uncertainty, and making data-driven decisions that are both reliable and reproducible.
General ML / Deep Learning / RL courses
This area focuses on classical machine learning, with a heavy emphasis on deep learning – the ubiquitous form of machine learning today, alongside reinforcement learning, which has gained significant prominence for its application in advanced reasoning agents and robotics.
Generative AI courses
This theme covers the area which has brought AI to the forefront in today’s world, helping push the frontiers of AI research and applications.
But please make sure to research any course you plan to take on student-maintained review websites.
Credentials Are Useful
AI has empowered people to independently (or in small teams) build amazing products, but the reality even today is that if you want to work at a company, or find co-founders for your AI startup, your credentials are very likely to matter. AI has elevated everyone’s baseline productivity and work quality to a certain level, and so one additional way to stand out further would be to gain good credentials. I want to highlight that the credentials from a reputed master’s degree are not ornamental – they truly show that you have the commitment and skills to go through a rigorous program out of your own desire for learning. Trust me, completing a program like this while working full time takes a lot of dedication and hard work.
This is backed by data on the degrees actually held by people working in software engineer, data scientist, and research scientist roles in the US. The workforce in data science and research roles is far more likely to hold a master’s or PhD, whereas traditional software engineering remains predominantly a bachelor’s-level field. The actual recent job postings requirements skew even more strongly towards graduate degrees for AI and ML roles, which the reader can find from proprietary data sources from hiring websites.
Image generated by the author. Specific numbers taken from the U.S. Bureau of Labor Statistics, Employment Projections, Table 5.3 (educational attainment of employed workers aged 25 and older, 2022–23 data; public domain). Percentages do not sum to 100% because lower categories (high school, some college, and associate’s degrees) are omitted. Note that the “Data Scientist” figure reflects a BLS category that combines data scientists with statisticians and mathematicians.
This, coupled with the fact that jobs have become more competitive, can make the case for the importance of getting these credentials. The following charts show the % change in job postings by role between 2020 and 2025 in the US. While general software engineering job postings have fallen, there has been an uptick in machine learning jobs.
Image generated by the author. Specific numbers taken from Indeed Hiring Lab: US Tech Hiring Freeze Continues, Experience requirements have tightened. whose data is sourced from their Github repo hiring-lab/job_postings_tracker from Hiring Labs (Creative Commons Attribution 4.0)
Good Academic Integrity Safeguards
Before I enrolled, I was a little concerned if the online nature of the program would somehow dilute the academic integrity that gets enforced in an in-person program. But soon enough I observed that my concerns were entirely unjustified. The following were two good practices that made me trust the academic integrity of the program.
Proctoring and Plagiarism detectors
Many exams in the course were proctored, which meant we would need to turn on our laptop camera, turn on screen sharing and then take the timed exam live. For assignments, automated plagiarism checks are commonplace today in most degrees, and that holds even in online programs.
Student honor code
As a student community, we would interact on chat groups and a university-created forum. In all these interactions, I consistently observed a healthy self-maintained honor code by the students which meant we collectively ensured that nothing that was discussed was against the code of conduct. For example, we always ensured that until an exam deadline was over and everyone had completed the exam, no one would discuss anything about it.
Lower Expenses And Opportunity Costs
With an online master’s degree, you get to continue to stay employed in your current job and pursue your degree. This means there is little opportunity cost associated with pursuing such degrees. You not only get to earn money through your job, but also maintain continuity in the industry and not lose promotion opportunities.
Here is a cost analysis considering various programs comparing the sheer magnitude of the tuition and opportunity costs, and how advantageous online programs can be. Not only are the tuition fees much lower for online programs, but also the saved opportunity cost of continuing to stay employed can be significant.
Image generated by the author. Tuition costs taken from university registrar websites (Georgia Tech Online, UT Austin Online, UT Austin on-campus, UIUC on-campus, UIUC online, Georgia Tech on-campus, Median US Software Engineer salary of $131,450 taken from US Bureau of Labor Statistics
Practical Challenges With Immigration
A few years ago, the traditional path for higher education in data science and ML was straightforward: you would find the best university program in the world they could get admitted to, complete the degree, land a great job, and earn enough to offset the time and money spent on your education.
In today’s world, however, immigration has become increasingly difficult, especially if you wish to get work experience in the host country after graduation. One piece of evidence of this is that work visa grants (which international students who choose to work after graduation) have fallen in the last few years in the UK.
Image generated by the author. Numbers taken from Immigration system statistics data tables provided by the UK Home Office (Open Government License)
Online programs, on the other hand, completely bypass these immigration rules – you get to do the program right from your own home country.
Disadvantages
Challenges With Doing Theses
Doing a thesis can be a great way to delve into research. AI is certainly an area under extensive active research (unlike the more traditional forms of software engineering). However, it is more challenging to network online and secure the backing of a professor for your thesis. Abhishek Divekar, a contemporary of mine at UT Austin, elaborates in this gist how he went about contacting professors via email, got accepted to write a thesis, and subsequently published a paper at EMNLP 2024. So, while it can be challenging, it can certainly be done.
No International Career Fairs
For international students completing the online program from their home countries, you are on own when it comes to job hunting since you won’t have local career fairs. However, this is unlikely to be a concern for most people. Like me, most people who do such programs are already working-full time, and aren’t necessarily looking for an immediate job change after the program.
Closing Thoughts
I certainly think such online programs are worth doing for people in the industry, mainly for the accelerated learning path that it puts you on, especially in the world of AI where things change everyday. It is imperative to keep up, and doing such a program can help immensely.
So I recommend taking the leap if all of the following conditions apply to you:
- You have a strong intent to learn specific subjects: Look at the program curriculum beforehand, and make a concrete list of courses that you are really interested in (for me, they were Reinforcement Learning, Generative AI, Advanced Linear Algebra among others). Look for your target university’s course review websites (for example, at UT Austin there was a student-run website of courses and reviews by previous students).
- You can get into a good program: Not all programs are the same in quality. It is wise to check how reputed the campus version of the same degree is, and if the same professors teach the online courses as well. Be wary of online-only programs which don’t have a strong in-campus version.
- You have realistic expectations: I think the primary motivation should be to gain deep foundational knowledge of the subjects. Applying those skills to advance your career would be a path that you will have to forge by yourself. Also, I don’t think doing such a degree will alone be enough to land your first job in the industry.
References
- This was CS50x by David J. Malan https://cs50.medium.com/this-was-cs50x-82be0995862b
- Jordan, K. (2015). Massive open online course completion rates revisited: Assessment, length and attrition. International Review of Research in Open and Distributed Learning, 16(3), 341-358.
- Khalil, H., & Ebner, M. (2014, June). MOOCs completion rates and possible methods to improve retention-A literature review. In EdMedia (pp. 1305-1313). Association for the Advancement of Computing in Education (AACE).
- Joyner, D. A., Isbell, C., Starner, T., & Goel, A. (2019, May). Five years of graduate CS education online and at scale. In Proceedings of the ACM conference on global computing education (pp. 16-22).
- The US Tech hiring freeze continues by Brendan Bernard, 2015 and The Experience Requirements Have Tightened Amid the Tech Hiring Freeze by Brendan Bernard (Hiring Lab), 2015:
- University websites with costs: UT Austin – Computer & Data Science Online (CDSO) | UT Austin – Registrar / Texas One Stop tuition rates | Georgia Tech – Bursar | Fall 2025 tuition totals, Georgia Tech – OMSCS program | UIUC – Office of the Registrar, Grad tuition 2025–26 | UIUC – Siebel School MCS tuition & fees page | UIUC – MCS Online (Coursera)
- US Bureau of Labor Statistics – Software Developers (OOH)
- Immigration system statistics data tables from the UK Home Office (Open Government License)
