Terms like Data Science (DS), Artificial Intelligence (AI), Deep Learning, and Machine Learning (ML) are more than just buzzwords in 2021. But, when it comes to MS CS/Data Science applications, there are many elements that might make the whole process overwhelming for the applicants — How does it feel to switch from MS track to PhD track? What do top universities look for in MS Computer Science and Data Science applications? How to improve your admission chances as a CS and Non-CS graduate?

Recently, I reached out to Dr. Arun Kumar (Associate Professor at the University of California, San Diego) to find out more insights from the perspective of a faculty member. He has been very kind to share advice and insights on what do top US universities look for in MS in CS/Data Science (and PhD) applicationshow to improve admission chances and general advice for MS in USA aspirants. Additionally, he also walks us through his own graduate school experience as an international student in the US.

My Graduate School Experience in the USA

What do Top Universities Look for in MS CS/Data Science Applications?

MS in Computer Science Phase

I went to UW-Madison for an MS at first because I was not sure if I was cut out for research. I did some research projects with a few faculty and ended up publishing a top-tier paper in my first 2 years. All that gave me the confidence to switch to PhD and aim for a research career.

Challenges during the Initial Phase of PhD

Due to various circumstances, I had to switch thesis advisors–three times, no less!

So, my PhD trajectory was pretty non-linear, filled with all kinds of uncertainty, and not as productive as I had hoped it to be.

At one point in the middle, I seriously considered quitting my PhD. But thanks to support from family, friends, and all my advisors, as well as obtaining help from a cognitive-behavioral therapist, I decided to stay the course to finish my PhD.

Things Improved with Time

The second half of my PhD saw me becoming more productive in large part thanks to my terrific advisors, the awesome supportive environment of the Database Group at UW-Madison, as well as no-strings-attached funding for my PhD offered by Microsoft Jim Gray Systems Lab in Madison.

I started proposing original problems and ideas and executed them well, resulting in more top-tier publications that are now widely read. I also collaborated with folks in the software industry to help transition some of my research ideas to practice and established new professional networks via conferences.

Finally, I also came out of the closet during the second half of my PhD. That also helped boost my confidence and creativity. I have blogged publicly about my coming out experience.

Choosing a Research Career in the Academia

In the last 2 years of my PhD, I was weighing industrial research labs vs academia. I was fortunate to get to work with junior students (BS, MS) on extensions to my PhD research.

I enjoy the process of mentoring strong students and seeing them grow intellectually to produce new ideas. I also got the chance to teach the UG DB course at UW-Madison in my last year. I found teaching enjoyable too. Due to these reasons, I decided to go for an academic career.

Getting into Data Science

During my graduate studies, my research area was already a part of “Data Science” (DS) to begin with. My work is at the intersection of data management systems and machine learning. Both of these areas are key pillars of DS.

So, I was naturally inclined to be actively involved in the formation and promotion of HDSI at UC San Diego to help define and transform the future of Data Science research, education, and societal impact.

Advice on Careers in Computer and Data Sciences

How to Choose the Right MS Computer Science Specialization

Editor’s Note: There are so many excellent options to choose from including Software, Cybersecurity, Cloud Computing, Data Science / AI-ML, Information Systems.

All 5 of those areas in the above list are great specializations with good career scope in both industry and academia.

  • My advice is to pick areas that excite you the most in terms of the intellectual content, the potential for impact on practice, and the day-to-day work involved.
  • One way to find out if research work in an area excites you is to read some recent research papers from that area’s top conferences and think if you’d enjoy being in those authors’ shoes.
  • If you want to pursue an industrial career, check out good courses in that area and see if you’d enjoy doing their programming assignments/homeworks.
  • MS Data Science: Hype vs Reality

    If you just run after the next shiny thing, you will likely end up as more style than substance. That said, misreading genuine longer-term changes as fads could lead to costly lost opportunities.

    Do your own research to assess if a change is longer-term or a fad. Two good signals I use to assess such things are the gradient and scope of said change across various stakeholders.

    For instance, I bet back in 2016 that deep learning was going to be a massive change in ML and started a major research project that eventually became the bulk of my tenure case at UC San Diego.

    Many senior faculty in computing, including world-famous ML experts, were still skeptical of deep learning in 2016. But I saw that many domain scientists, enterprises, Web, other companies were excited about deep learning’s potential to unlock unstructured data for analytics. I am glad my bet panned out well.

    Likewise for DS as a new discipline, UC San Diego too bet big by launching HDSI in 2018. Many universities were skeptical (some still are).

    But in the last 3 years, we have attracted top-notch faculty to HDSI, with some turning down many top 10 departments in CS and statistics! In the last two years, more top schools in the US have launched DS programs.

    The momentum is only growing, not just in academia, but also in industry in terms of the kinds of jobs that DS expertise can lead to.


Post Navigation