Andrew Marsee first became interested in programming in college and found himself gravitating towards classes where he could program. As a mechanical engineer for an engineering consulting firm in Cincinnati, he continued to seek out ways to program whenever he could and spent his free-time studying machine learning. When his job circumstances changed, he jumped at the opportunity to move back to Nashville and learn data science in a structured environment.
The NSS Experience
During his time as part of Data Science Cohort 2, Andrew loved working on real projects with local companies. “It was good to see what projects can look like and hear from people in the field of data science,” he shared. (Find out how your company can contribute data and data questions for our bootcamps.)
He also liked that there was always something more to learn at each step along the way. He explained that when he built his mid-course capstone project, his first Shiny app, he learned something new at each step in the process.
- Adding a drop-down menu
- Filtering the data and displaying it
- Adding a logo
Each new achievement made it fun along the way.
You get out what you put in. It’s a lot of independent study and the more work you put in, the more you’ll learn.
Do Rest Day Affect The Outcomes Of NBA Games?
Intrigued by the use of analytics in NBA team personnel decisions and in-game strategies, Andrew decided to do some analysis of his own for his mid-course project. You can read about his process and findings in our blog post Do Rest Days Affect The Outcomes Of NBA Games? | Discovery Through Data.
Topic Modelling On Open-Ended Survey Responses
For his final capstone project, Andrew wanted to work on a project that would not only give him more experience with Natural Language Processing (NLP) and Machine Learning but would be useful to the Tennessee Department of Education, who hired him as a data analyst half-way through the bootcamp. He met with his boss to come up with the right project.
Every spring teachers and administrators across the state complete a survey that is used to assess existing policies and inform future policies. Most of the questions ask the educator to score a statement on a Likert scale, but the final question is an open-ended one that allows them to express their thoughts on priorities for the Commissioner. This question resulted in 25,000 responses this year. By using machine learning and NLP, Andrew was able to identify themes, making the results much easier to digest. You can explore his approach to the problem and his findings on GitHub.
Landing the Job Early
A few months into the bootcamp, Andrew received a job offer for a position that he had applied to several months before the bootcamp began. While the job paid well, it would have brought him back to engineering. “At the end of the day, I enjoy data science and see myself doing that for the foreseeable future,” he shared.
So how did Andrew land a job mid-way through the bootcamp? Andrew took advantage of the network he was building through the bootcamp. A conversation with one of the guest speakers led to his job offer. Andrew is enjoying his new career and is continuing to learn with each new project.
Check out all of the recent grads on Data Science Cohort 2’s class website and hear the graduates share their journey into analytics and their experience at NSS in their podcasts below.