A STUDENT’S PATH FROM PSYCHOLOGY TO DATA SCIENCE

Julia Brooks has a five-year plan to become a data scientist. Ever since earning her bachelor’s degree in psychology with an emphasis on experimental design, she’s been interested in the power of data to deliver insights. However, when Brooks looked for jobs that would involve working with data, she found employers preferred to hire candidates with more technical degrees. Undaunted, she enrolled in Butler University‘s online Master of Science in Data Analytics (MSDA) program to acquire the technical background necessary to achieve the goals laid out in her career progression plan.

Less than a year into the program, Brooks is tackling challenging data analytics projects in class–and applying the lessons she has learned in the new data engineering job she found thanks to a connection she made in the program. 

We spoke with Brooks about her motivations for enrolling in the online MSDA program, her career goals, and her advice for future Butler University students.

What brought you to data analytics from a psychology background?

My degree was mainly focused on research and experimental design, so I had some experience with statistics for analyzing data from experiments. It felt like data analytics was a good direction for me, but when I went out into the workforce after earning my psychology degree, employers wanted people with more explicit technical degrees for the positions I was interested in. I kept looking specifically at data analysis roles.

What led you to choose Butler University for your data analytics master’s degree?

I liked that it was a program for working adults. Before applying, I watched presentations by some of the professors, and I really liked their passion for the field, their approach to the practical applications of analytics, and what they wanted their students to be capable of as soon as they came out of the program. Butler also made the supply chain course a priority in the business track because of all the supply chain issues we’ve had since COVID. That showed me that they’ve been paying attention to trends.

Do you have a favorite course so far?

Yes, the data engineering course, DATA616. I liked it because it challenged us at the point we were at in our development as programmers. It made me stretch the most, and I felt like I learned the most about general computing and how to handle big data sets. The course also relates to my data engineering job. For example, I use SQL and query databases—although the databases at work are already created. The course also focused on putting together data sets.

Did hands-on project work help you get your data engineering job?

Yes. I think that’s a big part of why they hired me. First, one of my classmates recommended me for the position after we worked on a group project together. Then during my interview, I presented some of my code from that project, and they were really impressed with it. The project gave me an opportunity to prove that I knew what I was talking about.

One of your biggest projects for the program was a web scraper, a tool used by companies such as Google to index the web. What was it like creating that project from scratch?

The professor handed us HTML code for about 1,000 websites. We had to pull all the URLs from those websites and put them in a spreadsheet, breaking each URL into its components. The project was all about grappling with big data that had a lot of variety to it. 

I started with a quick crash course on HTML and how URLs are constructed. I had to break down this big problem into smaller steps to make it more manageable. A big part of data analysis is becoming a subject matter expert to guide your analysis. In this case, I had to understand what each part of a link signals and learn enough HTML to pull the relevant information out of any HTML document.

I did something similar with a business analytics project. I had some subject matter expertise in business and did a project on stock market trends, so that background knowledge really helped guide my analysis.

How does your data engineering job relate to the skills you’re learning in the program?

Data cleaning and validation are a big part of the job. That’s making decisions about what data is valid, and that’s a focus of the program. The work has also helped me practice the SQL skills I learned in the program. The things I learned in class were enough for me to start in the position, and the theoretical knowledge I learned gave me a foundation to build off of at work.

You’re interested in becoming a data scientist. How is the program supporting that goal?

Data science is all about exploration and investigation, and those skills are emphasized heavily in the program. They teach you a data set, but then they set you free and you can run off and come up with your own questions and investigate. The program supports the exploratory aspect of data science.

The job I got through the program is also helping me become a data scientist. I’ve been working with my manager in my current position to achieve that goal. So for the first year, I’ll work as a data engineer. Then I’ll transition into a role that’s halfway between data engineering and data science. Within three years, I’ll move onto the data science team, which puts me ahead of schedule for my goal.

How does your employer support your data analysis education?

They knew when I started that I was in this program because I got the job through the program. I was able to communicate with them about my academic workload, and they’ve been very understanding about what I have to do and careful about not giving me more work than I can handle during work hours. I’m also keeping them updated on different projects I’m working on in the program, and they’re excited about finding a place for those skills within the company.

You’re working full-time while earning your degree. How do you manage your time?

I’m lucky that my full-time work is my only other big obligation. I usually spend between 10 and 20 hours a week on coursework, so I can set aside a couple of hours each night and a chunk of a weekend day, and that’s usually enough to stay on top of everything. I had to figure out where I wanted to make sacrifices, but the big part is just making sure that you don’t have to have to make a critical sacrifice. I also recommend getting enough sleep to stay focused.

Are you getting to know your fellow students? What are they like?

Several of my classmates currently work in technical fields or data analysis. It’s been really good to talk to them and work with them in these shared experiences, and it has solidified my decision that this is the path I should be on.

What advice would you give to professionals considering a data analytics master’s program, but who don’t have a strong technical background?

Data analytics is more about having a curious mind and a desire to solve problems than explicit math skills—because those can be taught. They teach you how to do the technical things in the program. As long as you’re interested in searching data for answers or using it to guide your organization, this is a good program for you. It’s a significant time commitment, but it’s worth it.