An Applied Analytics Student's Journey From Electrical Engineering To Cloud Computing | Columbia...

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Oche Oche-Obe, a former technology consultant at EY in Nigeria, has long been passionate about data and problem-solving. After gaining hands-on experience in the consulting field, Oche decided to pursue an M.S. in Applied Analytics (APAN) from Columbia University. His engineering background, which emphasized precision and analytical thinking, inspired his transition into the world of data. Last fall, Oche was appointed an at-large representative in the SPS Student Government, further demonstrating his leadership and commitment to the SPS community.

In a recent interview with SPS, Oche reflects on how his engineering and consulting experiences shaped his approach to analytics, the value of hands-on learning at Columbia, and his aspirations for using data to drive impactful solutions.

The program explores a broad spectrum of the analytics field. The core courses lay a foundation for what you should know to get started, and the electives allow you to tailor your curriculum to align with your personal interests. The lecturers in the program have experience in various parts of the analytics field and are available for you to learn from and gain insights about what part of the data world really captures your interest.

The "Predictive Analytics Competition in Frameworks ⁘ Methods" assignment was a significant project for me. It is a data modeling competition across the entire cohort during the first semester of the program, complete with a leaderboard ranking based on your best model's performance. While I had prior coding experience and a foundational understanding of machine learning, the class was my first experience coding with R and really diving deep into data modeling concepts. I had to double down and push my limits if I wanted to rank high among more than 400 people! That experience set the tone for me moving forward––that I was going to have to go above and beyond wherever possible throughout the course of the program.

The emphasis of my electrical engineering degree was in power systems. Even though data and power systems seem very disconnected, I have always known that you can apply data anywhere to make things more efficient (though some data applications in the energy space certainly surprised me). Not only are analysts crunching the numbers to figure out new energy-saving techniques, but also I was pleasantly surprised to see how concepts I learned in APAN like anomaly detection and predictive modeling today can help the energy industry predict faults and alert companies to address them before they materialize. Maybe it is well known in that space too, but you can use data from smart meters across an entire grid to carry out load balancing more efficiently based on the time of day or season of the year. Engineering taught me the basics; analytics showed me how best to apply those concepts in practical situations.