Alumni Spotlight: Kweku Ulzen, Senior Data Scientist at Nielsen

Alumni Spotlight: Kweku Ulzen, Senior Data Scientist at Nielsen

Posted by Ariella Brown

Updated: Nov 5, 2018



Mastering the Quantitative Side of Computing

Kweku Ulzen came to NYC Data Science Academy with a strong technical background and a goal to master the more quantitative side of computing. Kweku earned a degree in Management Information Systems and then went to work in Microsoft's Consulting Services division. He did that for three years before shifting to a job as a Premier Field Engineer supporting IT infrastructure strategy and business operations for customers. While the role gave Kweku valuable experience in the tech world, he soon realized that the career was not as quantitative as he would like. Kweku made the decision to pivot his career toward data science, and we went on the enroll in NYC Data Science Academy.

Thanks to the knowledge and experience acquired at NYC Data Science Academy, Kweku landed a position as a Senior Data Scientist at Nielsen soon after graduating. In his new career, Kweku enjoys the best of both world; he encounters data-driven challenges everyday, and is also able to put his previous experience with cloud computing to good use.

We sat down with Kweku to learn more about why he chose to pursue data science, the experience at NYC Data Science Academy, and his new role at Nielsen.

1. Could you fill us in on your educational and career background before you decided to pursue data science?

I graduated from University of Alabama with a degree in in Information Systems Management. I worked as a consultant at Microsoft for a few years and then transitioned into the position of Premier Field Engineer: Data & Artificial Intelligence. I then decided that I wanted to redirect my career toward data science and resolved to focus on that.

2. Why did you decide to study data science?

I was always interested in data and analytics. When I began working for Microsoft, I was placed in a more technical, less analytical role. While I did get to shift to some of the more quantitative aspects of computing, I felt that if I stayed at Microsoft I would not be able to redirect my career in the direction I planned. I decided the best route for me to take would be to focus on studying data science. That’s why I decided to enroll in a bootcamp.

3. How did you find the learning experience at the NYC Data Science Academy bootcamp?

The learning experience was great. In addition to the data science skills I learned, I gained a great deal of confidence in my mathematical ability. I was not as certain in that area when I came in as when I came out.

4. How did you find the other students?

The bootcamp draws people from many different backgrounds. There are students with strong research background, and those who have degrees in subjects like physics. NYC Data Science Academy also attracts students with more of a coding or math background. What's great about this diversity of knowledge is that the students serve as an educational resource. I found that I could lend my strengths to the cohort, and I was about to tackle particularly tough challenges thanks to my peers. For example, I had to draw on a lot of mathematical knowledge that I hadn’t used since my college days and also learn new things. It was so important to have the extra support coming from students that were very firmly grounded in this area.

5. How did you find the projects you worked on during the bootcamp?

The first project was a data visualization project that took synthesized data from different neighborhoods in Los Angeles and New York City in order to discover which neighborhoods are most similar. The second involved web scraping from sites that sold tickets for events like concerts and sports. One challenge I encountered is that many sites are set up to prevent scraping, which forces one to find a way around the obstacles they have set in place. I was able to improvise lessons and find different ways to work around them.

The third project was a group project involving machine learning, which predicted housing prices in Ames, Iowa. I particularly enjoyed that project because we got to dive in and learn, working on new methods of solving problems. I also found it stimulating to be on the team and keep up with teammates.

At the very end, we had another group project, the capstone project. I worked with a large organization to help them understand how consumers were using their product.

6. What did you like best about the bootcamp experience?

I really enjoyed getting to know the people all around me and working with such a diverse group of students. Thanks to NYC Data Science Academy I have gained a much larger network, which has been invaluable as I grow my career in data science.

7. How did you find your job search experience?

The job search was really positive for me. I found it to be a smooth sailing experience! While I was still in the bootcamp, I was headhunted by one company just as a result of changing my LinkedIn status. Though that did not turn into the job I took, it was really a good starting point for me. Within a month, I had secured a job offer.

8. Can you tell us about your new job?

I’m working as a data scientist at Nielsen on the buy side of the operation. That’s the side of the company concerned with what people buy rather than what they watch (what people usually think about in context of Nielsen ratings). I work on pulling information from panel users and purchase submissions. I’m able to dive in and troubleshoot issues with SQL queries and with an area of cloud computing that many other data scientists are not as familiar with. I’m glad to be able to use that expertise from my previous work experience.

9. Do you have any advice for those who are planning to pursue a career in data science?

Yes, first I’d say to make sure that it is the thing that you want to do. Begin your investigation by being a self-starter and doing research and practicing core concepts. Then you have find the best solution for you. If you find that you need more structure after completing your self-study, look into top-rated data science programs and bootcamps.

10. What would you tell those considering this bootcamp?

Good luck, it’s not easy! It’s the hardest three months you’ll ever experience. But at the end, it’s really worth it. My advice to be prepared for a very tough, but rewarding experience.

This post was first released on SwitchUp.

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Ariella Brown

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