Are you aspiring to be a data scientist, but confused with the data science career path? Is it worth pursuing MS Data Science, or rather stick to MS Computer Science or Statistics? Let’s find out.

There are multiple ways of becoming a data scientist. You could do a specialization in Statistics, Economics, Computer Science or Data Science. If you are not too sure about the data science career path, this blog post will definitely provide you with some insights. In order to bring in student perspectives, I reached out to Aleksa Basara, a current Masters student at Cornell. Aleksa is a currently pursuing the MPS program in Applied Statistics (Data Science concentration) – one of the best Masters Programs in the US for Data Science.

MPS Data Science after Double Major in Statistics & Economics at Cornell

Tête-à-Tête with Aleksa Basara on Data Science Career Path & Program Experience

Tanmoy: Could you please share your College Admission story? How did you figure out that Cornell was the right choice for you at the Undergrad level?

Aleksa: Growing up, I always knew I was more interested in quantitative subjects, such as math and computer science, but never had a clear favorite subject that I would later major in.

When researching colleges, I really admired that Cornell offered a wide variety of courses and majors to choose from. I could explore what interested me and let my passions reveal themselves. In the end, I was able to explore classes ranging from computer science to statistics to French within my first few years at Cornell.

I decided to double major in Statistical Science and Economics, but I never could’ve made that decision without the opportunity to try other fields as well. Furthermore, the offerings didn’t end with academics, as I knew Cornell had a wide variety of clubs and organizations to become a part of. From the beginning, there was a good promise of both strong academics and good balance across different aspects of my life.

Tanmoy: What do you enjoy (hobbies & interests) outside studies and work?

Aleksa: Outside of studies and work, I enjoy Swing Dancing and playing/watching different sports. Tennis has always been a big one for me.

Tanmoy: You were pursuing a Double Major in Statistical Science& Economics. How did you end up with Professional Master’s in Applied Statistics (Data Science Concentration)?

Aleksa: Both undergraduate majors have prepared me to bring a unique perspective as an analyst to a wide variety of problems. They’ve challenged me to deconstruct problems, evaluate evidence, and be able to contribute to the creation of a solution.

Before ending up at the MPS program, I recognized an increase in demand for technical competencies to solve many of today’s problems and thought it would be useful to pursue a program that not only taught statistical concepts but taught the students how to adapt in the face of new technologies.

When I saw the course offerings and the capstone project description for the MPS in Applied Statistics at Cornell, I knew it would be a strong fit.

Tanmoy: Why didn’t you go for a job straightaway after Bachelors?

Aleksa: Generally speaking, I think on-the-job experience is the best teacher for most careers. However, advanced degrees are good ways of signaling passion and competency in a field, which can be valuable early on in getting the first few jobs and excelling while there. I knew this program will matter in the future.

I wanted to do a program that provided rigorous classes, allowed me to practice while learning in the form of a capstone project and be surrounded by great peers. When I found out about this program, I decided it was the right step before entering the workforce full time as a data scientist.

Tanmoy: Why did you choose MPS Applied Statistics (Data Science concentration), a Professional Master’s Program (PSM) over MS Data Science?

Aleksa: For me, the content of the program was more relevant than the name it would go by. While I believe computing has an important role in the study of data, I think knowledge of the statistical theory behind some of our favorite packages is essential for selecting the right tools for the problems we solve. For this reason, it was a requirement for any program I chose to have a good mixture of both. Applied Statistics programs tend to place a larger emphasis on statistics than Data Science programs from what I’ve seen, so the fact that my program was able to provide teaching in statistics while acknowledging the importance of computer science was a huge plus for me.

Tanmoy: Your feedback on the Bachelor and MPS programs at Cornell.

Aleksa: The two complemented one another well in my opinion. During my undergraduate, I was taught a lot of statistical theory and gained some exposure to computing on the side. During my Master’s, there was much more opportunity to apply what I learned in both statistics and computer science. One of my favorite aspects of the MPS program was the chance to work with a company on a project that had real implications for stakeholders as students. It taught me a lot about communication and generally good practices for working on data projects within a team.

Tanmoy: Besides ranking, reputation, expert faculty members and state-of-the-art facilities, what else did you like about Cornell?

Aleksa: The students were definitely great. I think having one person in your life with the passion for what they do and a strong work ethic is inspiring. Being in a room filled with such people let me see that anything is possible when the right people get together. That being said, friends and peers are definitely at the top of the list for things I liked. Let’s put food and scenery at #2 and #3.

Tanmoy: Since Data Science is hot & trending these days, quite often students consider pursuing Data Science or Data Analytics at the Undergraduate level. Do you think it’s a good option to micro-specialize at the undergrad level?

Aleksa: I believe that you don’t know what you’ll like until you try, and by specializing too soon, you may be restricting what you’re willing to try. From my point of view as a student, Data Science/Analytics undergraduate programs don’t necessarily take away this choice, but it’s up to the student to also gain exposure to a variety of topics to help them decide what kinds of problems interest them and what kind of career they want to have.

I think there are many success stories of people going into Data Science without degrees in computer science or statistics, which are currently the most common paths. I think most Data Science/Analytics positions require at least some basic competency in CS/Stats, so as long as the student can circle back to those topics somehow, how specialized they become will depend on their goals and interests.

Tanmoy: Quite often students struggle to decide between Data Science & Data (Business) Analytics. What would be your advice for the students in choosing the right subject at the Masters (MS) level?

Aleksa: Reflect on where you want to be and what you want to do professionally. Find out what excites you and what you value in a given position. Be critical about what skills and qualifications you already have. Finally, evaluate what you need from that program to make it worth your while. It may be the skills you walk away with, the connections, or the work experience. The name of the degree is only one small aspect.

Tanmoy: Today we have got several MOOC courses in Data Science. What are your thoughts on MOOC courses – how beneficial are these courses?

Aleksa: Not applicable as I haven’t taken any yet.

From what I’ve heard, if largely relying on MOOCs for learning, supplementing the courses with separate side projects is useful for demonstrating your ability.

Tanmoy: Data Science has been in huge demand. But, some people also call it over-hyped. According to them, it’s better to pursue MS Computer Science or Statistics. What are your thoughts on this?

Aleksa: Many of us interested in DS have seen some form of a three-circle Venn diagram, with Domain Knowledge, Statistics, and Computer Science being the core three circles of Data Science. That being said, Data Science will mean different things for different companies depending on which circles they need to be supported at the time; hence, the “Data Scientist” could fill many different roles.

Which degree is best (Data Science vs Computer Science vs Statistics vs etc) largely depends on the role one is looking to fill. Someone more interested in databases and the production of code may be more interested in pursuing a CS degree. Someone more interested in what the data mean and how to properly gather them may be more interested in a Statistics degree.

There’s no question that both provide a great foundation for a career in DS as they are both relevant quantitative fields. However, it’s still completely possible to be a Data Scientist with another degree provided they are willing to learn what they are missing to land whatever dream position they have in mind.

Tanmoy: Your two cents for the folks who want to take up Statistics and Data Science as their Major.

Aleksa: Understand that you don’t reach the end of the road to becoming a data scientist with a specific degree. Degrees move you along and help you move further, but there will always be new things to learn since DS is such a broad field. So, be patient, be honest about what you enjoy, and take it one step at a time.

Related Articles:

R or Python – Which one should you learn first?

MS Data Science vs MS Business/Data Analytics

Fall 2019 MS Application Deadlines for US Universities

45 Best Universities for MS Analytics in USA

How to Become a Data Scientist & ML/AI Developer

Top Platforms & Resources to Learn Data Science & Machine Learning Skills

17 Best Online Courses on Machine Learning, Deep Learning, and Artificial Intelligence