Since launching in 2016, the University of Illinois online MCS-DS program has quickly become one of the top online data science master’s programs in the U.S.
That’s not a big surprise. The course comes from the same faculty as the on-campus MCS program, which is a top 5 graduate computer science school, according to U.S. News and World Report. Plus, the program – offered completely online through Coursera – can be pursued part-time, offering an ideal format for working data science professionals.
Today, we’re taking a closer look at UIUC’s MCS-DS program in order to get an overview of the program, admissions and academic requirements, and the perspective of a UIUC graduate, who now works in the San Francisco Bay Area in data science.
Here are a few reasons why this is such a popular master’s of data science program:
One potential drawback is the limited course selection. The data science track focuses on four areas: machine learning, data mining, data visualization, and cloud computing. If you’re looking for in-depth courses on deep learning or computer vision, you might be underwhelmed.
Course requirements include:
Students can pursue the degree full- or part-time. The program can be completed in as little as one year, but students have up to five years to finish all coursework.
Roughly 30% of applicants are admitted into the program, and the requirements are fairly standard for data science programs. You don’t have to take the GRE for this program (although it can help you earn admittance). To apply, applicants must have:
Note: If you don’t have prerequisite coursework in these areas, but have professional experience, there are accelerated computer science courses you can complete.
The UIUC program is 100% virtual, with courses based on the Coursera platform. Each course is made up of modules that feature video lectures, as well as supplementary readings, video lectures, assignments and discussion forums. This isn’t a self-paced course; rather, modules are unlocked throughout the semester.
There are also live classroom sessions and live office hours. Exams are conducted through the ProctorU platform. Students are admitted into Slack groups to connect with their peers, and Zoom is also used for communication.
We talked with Apurva Hari, a San Francisco-based AI consultant currently working on the chatbot team for a large national bank. Apurva was a part of the first UIUC cohort in 2016 and graduated from the program in 2018.
I graduated from a college in India with a degree in computer science, and my first job was with a company called Franklin Templeton Investments. I was a part of the security and customer experience/networks teams.
I was an analyst on that team. I moved to the Bay Area in 2015. I took up a certification course at UC Berkeley Extension, Big Data Analysis, and took courses in Python programming and the fundamentals of machine learning. When I joined the master’s program, I worked as a lead data analyst on the data governance team at Silicon Valley Bank.
It was online and part-time. I applied to a few on-campus programs before that and was admitted, but this program allowed me to continue working while I studied.
When I applied, I had to take a GRE and TOEFL exam, since I’m not a U.S. citizen. I had applied to other programs, so I already had those credentials. I also had to submit a statement of purpose, transcripts, letters of recommendation, and certificates.
Note: The GRE is no longer required for the UIUC program.
My coursework was primarily focused on statistics and data science. I was taking courses in statistics at a basic level, advanced stats, Bayesian statistics, applied machine learning, data visualization, and data cleaning.
One course that is very close to the work that I do now was Text Information Systems (see a sample syllabus). This covered the basics of text mining, all the way to advanced concepts like building classifiers and text retrieval algorithms.
These concepts have been really, really helpful. The course gave me a theoretical perspective on data visualization, as we covered things like building better dashboards, design fundamentals, and color theory.
When I had completed about 75% of the program, I started applying for jobs. And I joined Wells Fargo, initially on the AI team. I think part of why they hired me was this degree and my background in data science from the program.
I really enjoyed the program, but if someone is going down this path, I’d say that it’s a lot of work, especially if they’re not from a technical background. Also, if you’re working, you really have to know how to manage your time well.
At the time, there were some technical issues with the videos and exams, but typically those were resolved.
Another drawback was that, at the time, they didn’t really have any location-based career services. As I was based in the Bay Area, I had to navigate the job market myself.
I would also say that the course selection was limited. There’s a lot of helpful machine learning content, but deep learning was only touched on in a few courses.
I had all the typical benefits of an on-campus master’s student, but they were all virtual. I had access to recorded seminars and, access to the e-library, and one great benefit was that I received discounts for conferences. I was able to attend several conferences in the Bay Area.
I would definitely recommend this program. The degree has helped me successfully transition to a career in data science. Plus, I got the opportunity to interact and learn from a talented, motivated, and extremely supportive team of peers and staff (professors, TAs).
And the most special reason: I graduated with an advanced degree from one of the top universities for computer science (which has been a dream) despite having a baby around that time.