In one of our previous posts, Prof. Arun Kumar talked about the intricacies of graduate school admissions in computer science and data science. In this post, he shares his thoughts on studying computer science and data science after high school. More importantly, he shares his thoughts on “is it worth doing a major in data science after Class 12“.
Careers in Computer Science and Data Science after Class 12
Q&A with Prof. Arun Kumar, Associate Professor, UC San Diego
Q. What motivated you to opt for Computer Science after Class 12?
Arun: I enjoyed programming, databases, and information management in my high school CS course. CS was/is a perfect blend of the abstract and the concrete. We blend abstract mathematical/conceptual thinking and reasoning with concrete software to help people perform various tasks in the real world. That and I realized that computing was going to become ever more crucial in humanity’s future.
Q. What would be your advice for high school students who are interested in Computer Sciences and Data Science?
Arun: Solid programming and scripting skills are a must for both. Python is now widely useful in both CS and DS. For CS, I also recommend learning C++ and/or Java in addition.
For DS, I also recommend learning SQL and the APIs of popular Python libraries (NumPy, Pandas, Matplotlib, and Scikit-learn) in addition.
Apply the above skills to challenging problems, e.g., on programming contest portals such as TopCoder for CS and data analytics contest portals such as Kaggle for DS. These can be great CV points.
Pick up good communication skills, both oral and written, as well as critical thinking and the ability to put yourself in others’ shoes. I have found that humanities courses that involve debates, essays, talks, etc. really help on this front. Many people underestimate the importance of such skills in STEM in general.
Q. What would be your advice on choosing the right specialization in Computer Science (e.g. Software, Cybersecurity, Cloud Computing, Data Science / AI-ML, Information Systems, etc.)?
Arun: All 5 of those areas in your 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/homework.
Q. Do you think it’s a good idea to major in Data Science at the Undergraduate level? Isn’t it better to specialize in comparatively broader disciplines (e.g. Physics, Statistics, or Computer Science)?
Arun: It depends on your career goals and the university. A DS program will offer more statistics/math skills and hands-on experience with data-driven applications (say, in a domain science with messy, real-world datasets) than most CS programs. All that can give you a headstart for careers as a data scientist, ML engineer, etc.
A CS program can also lead to such career pathways but it will likely need a lot more conscious independent effort on the student’s part to fill in gaps in their statistical/math knowledge and avenues to obtain hands-on experiences. I’d be wary of universities that simply tack on some CS courses to a statistics degree or vice versa to jump on the bandwagon without deeper pedagogical thought on the curriculum.
Look for programs that are carefully designed to achieve a true synthesis of the underlying disciplines to train “Data Science-native” thinkers. This is what we have done/are doing at UC San Diego HDSI with our BS in Data Science.
For a hands-on experience, all of our DS majors work on a capstone project in the final year. HDSI also just launched our PhD, MS, and Online Masters degree programs in Data Science. I see this as a paradigm shift in the future of Data Science in US academia in general.
Related Article: Top 20 Institutes for B. Tech in AI in India
Q. What are the upcoming trends in Data Science and AI/ML that students should be aware of?
Arun: In core ML/AI algorithmics, there is much focus on reducing the bloat of deep learning models with more domain-specific invariants such as physical laws in spatiotemporal analytics and text semantics in NLP.
In ML/AI theory and statistics, there is much focus on rigorously explaining why deep learning methods work so well in practice, quantifying their robustness and security, and ensuring Data Science methods do not cause discriminatory impact across various groups of people.
Finally, building systems and data management tools for ML/AI workloads, data-centric tasks in ML/AI, end-to-end ML platforms, MLOps, and cloud-native tooling for ML/AI are all hot areas in both research and the software industry.
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Featured Image Source: Rasmussen University