At The Data Incubator we run a free eight-week data science fellowship to help our Fellows land industry jobs. We love Fellows with diverse academic backgrounds that go beyond what companies traditionally think of when hiring data scientists. Armand was a Fellow in our Fall 2016 cohort who landed a job with KPMG.
I received my Bachelor’s degree in Mechanical Engineering from NC State University. After college, I became a management consultant specializing in program and strategic management. As a consultant, I saw the value of data-driven decisions and extracting insights from data. As a result, I decided to go back to school to obtain my Master’s in Systems Engineering. There I was introduce to R Programming software, data mining techniques, and applications of optimization. My Masters not only exposed me to data science, but it also provided me a framework to approach complex problems.
What do you think you got out of The Data Incubator?
Coming from industry and trying to make a career switch, the most valuable thing I got from The Data Incubator was the technical experience to support the credibility I needed for my career transition. As a management consultant, my professional history was lacking “true” data science experience. The Data Incubator is a respected institution, and requires its fellows to absorb an exorbitant amount of knowledge and showcase those skills through daily coding challenges, weekly mini-projects, and a final capstone project.
Could you tell us about your Data Incubator project?
The inspiration for my capstone was rooted in my upbringing. I was born and raised in North Carolina and went to school at North Carolina State University, which is located on “Tobacco Road” – The Heart of College Basketball. That is why I chose to explore college basketball game data and I used it to develop and upset classifier. My app presented insights on Division I Men’s College Basketball games as it related to identifying upsets throughout the college basketball season. I built a support vector machine classifier, which was trained using the previous three season, accounting for over 17,000 data points. I provided three modules for the user. The first module provided the probability of an upset for each scheduled game. The second module provided the correlation between game statistics and the upset probability. The third module showed the models performance on predicting NCAA Tournaments.
What advice would you give to someone who is applying for The Data Incubator, particularly someone with your background?
The Data Incubator provides a bridge to transition into a career in data science. For those applying who have a similar background as I, begin to identify how you can transform your current projects to take a more data science approach and implement those ideas if you can. If you are accepted, utilize the 12-day preparation course as it provides a lot of information, and if you are like me a lot of it will be new to you.
What’s your favorite thing you learned while at The Data Incubator? This can be a technology, concept, or whatever you want!
Although I could name a number of favorite things I’ve learned, to be brief I will share only my favorite concept and my favorite interview prep. My favorite concept was MapReduce and distributed computing. I found the concept interesting because it provides a way to manage data that is too large to store in local memory. My favorite interview prep was the daily coding challenges. These challenges expose me to several algorithms and data structures that I had no prior experience with. It also forced me to take into consideration the time and space complexities of my algorithms. These were all topics focused on during my technical interviews.
Where are you working now and tell us a little about your new job!
I have been hired by KPMG, a professional service company and one of the Big Four auditors. KPMG has hired me as a data scientist in their Lighthouse Data & Analytics Center of Excellence. I will be helping KPMG execute their strategy in helping leaders embed data and analytics in every aspect of their businesses, including how decisions are made, how processes are operated, and how people are enabled.