Edgar Pal, Class of 2019: The MPCS is perfect for CS career advancement.

The University of Chicago Masters Program in Computer Science students push boundaries and innovate across many facets of industry. Whether it’s developing seamless UX interfaces, engineering software at Fortune 500 companies, working in big data or keeping networks secure, our students use their applied-skills education from the MPCS to problem-solve, create, and elevate the computer science field. Learn from their stories and discover how a CS background can prepare you for cutting-edge careers and leadership roles.

Edgar Pal University of Chicago MPCS

Edgar Pal (AB ’13, MSCS ’19)  is a Senior Data Scientist at Allstate. He walks us through a great day at work, why he’s excited about autonomous vehicles, and how he took his knowledge from the Natural Language Processing class to apply text mining techniques to an algorithm at work.

What does a great day at work look like for you? 

I start my day by meeting with my trainee. He's new to the company, so we've assigned him a pricing project that will help him get familiar with Allstate's data and modeling processes. I ask a few questions about his progress, satisfied that he knows what to do next, then I offer suggestions on how he can fine-tune his model. Later in the morning, my team conducts a sprint review, where we present our projects and ask our business partners for feedback. It's a productive meeting, with a lot of fruitful conversations, and we get some great questions that prompt us to think about the projects differently. Later that morning, I meet with my teammates and manager for our daily scrum, where we give progress updates and identify any issues that need to be resolved. 

My afternoons tend to have fewer meetings, so I get to dedicate that time to more technical work. It's important for me to stay focused while I'm writing code and building models, so I put on my headphones and listen to my favorite piano covers. Sometimes I'll get stuck on a coding issue, or I'll forget a specific modeling function I need to use, but a quick search on Google usually solves the problem. I ask my teammate to review my code, and I complete my work planned for the day. I feel a great sense of accomplishment as I mark a task as "done" on our sprint board, which we use to keep track of progress on all of our team's projects. 

What do you enjoy most about data science? 

Data science is such a rapidly evolving field, and there's always something new to learn! Every Friday, I get to take a break from work and dedicate a few hours of my time to improving my skill set and learning about new topics in data science.

Which programming language/technical skills do you use most often at work? 

I use R and Python to pull data and build models. I also use Hadoop, Hive, and Spark to work with large datasets.

Describe a data science problem at work that your MPCS knowledge helped you solve. 

Allstate needed to update its search engine on the Allstate.com website, and I was tasked with building an algorithm that would improve the results according to a user's preferences and interests. I had taken Natural Language Processing with Amitabh Chaudhary the year before, and I was able to apply the text mining techniques I learned from that class.

Did you come to the MPCS with a computer science background? What motivated you to apply and enroll? 

No, I didn't have a computer science background before I joined MPCS. I had graduated from UChicago with a double-major in economics and public policy, and my first job after college was an actuarial analyst position at Zurich Insurance. I thought that I would pursue a career in business and economics, until I realized that I enjoyed the more technical aspects of actuarial work, such as programming and predictive modeling.

Once I decided that I wanted to become a data scientist, I applied to MPCS for two reasons. First, having a graduate degree makes it much easier for you to be considered for data science roles. Second, the computer science education filled a gap in my knowledge and skill set. Data scientists often work at the intersection of statistics and computer science. I had already learned a lot of statistics through my economics degree and actuarial experience, but I didn't have formal training in computer science.

When I learned that MPCS offered a specialization in Data Analytics, I knew that it would be the right program for my academic and career goals.

What about the future of the data science/tech industry most excites you? 

I'm really excited about autonomous vehicles, as everyone will benefit from having safer cars on the road. This is especially important to us at Allstate, as we need to adapt to the changing market for insurance products, and I'm sure other companies in the auto insurance industry feel the same way. 

Please tell us about attending MPCS as a part-time student. 

I was working full-time as a data scientist while studying part-time in MPCS. Because my goal has been to advance my career in data science, it was important for me to continue to get that work experience, rather than give up that role to study full-time.

Allstate offers great flexibility and work-life balance, so I was able to ensure that I would leave the office between 4 and 4:30 p.m., so that I could arrive at Hyde Park by 5 p.m. This gave me enough time to grab dinner, buy a coffee, and review my course materials before class began at 5:30 p.m. Almost all of our classes begin at 5:30 p.m. on a weeknight, so the schedule is convenient for working professionals like me.

Studying part-time wasn't easy, however. There was a lot of homework and studying to do in the evenings and on weekends, in addition to my full-time job responsibilities and other life commitments. I'm glad that I had support from my team at Allstate and my family at home.

Would you recommend the MPCS to others? 

If you're looking for advancement in your professional career in a computer science field, then the MPCS is perfect. The program offers specializations in data analytics, software engineering, high performance computing, and other areas. However, if you're more interested in academia or research, I'd recommend going directly for a PhD education that emphasizes the theory a lot more.

What is your favorite memory from your time as an MPCS student? 

Working on a C Programming final project with one of my classmates at a cafe in Wicker Park. There was a lot of work to do, and we stayed at the cafe, furiously typing away and debugging code until we stopped around 1 hour before the deadline. We became close friends and still keep in touch today.

What was your favorite MPCS course? 

Natural Language Processing with Amitabh Chaudhary. Although I had already been working as a data scientist for 3 years, I had no prior experience with NLP, and it was interesting to learn a new topic in data science. My final project involved the analysis of political tweets, which allowed me to combine my interests in politics, social media, and NLP.

How has your MPCS education helped you achieve your professional goals? 

Employers of data scientists usually prefer candidates who have graduate degrees, so MPCS certainly helped expand my opportunities for career advancement. The data analytics electives -- Machine Learning, Natural Language Processing, and Advanced Data Analytics -- were especially relevant to my data science career. Plus, the computer science background has given me a different perspective on algorithmic thinking and machine learning, which sets me apart from my colleagues who came from statistics and other quantitative programs. 

What is a piece of advice you’d give someone considering applying to MPCS? 

Attend an info session to learn more about the program before you apply. I attended a presentation given by the program's directors, and it was very helpful. You also might want to consult close friends and colleagues. A teammate of mine strongly recommended that I apply, and I'm glad I considered his advice.

Do you have any career advice for someone who's pursuing a job in your current field? 

Always be open to learning new things, and be humble. Data science is a rapidly evolving field, and no data scientist is expected to know all of the techniques at our disposal.