From the Ph.D. track to Data Science
Recently our team at Springboard sat down with Diana Xie, a Data Science Career Track grad and former Ph.D. student, to learn about her experience in the course and decision-making process during her career transition. Read the Q&A below to learn about her insights.
What were you doing before Springboard?
I was a Ph.D. student in neuroscience. I had just qualified for a Master's when I finally decided I wanted a change in careers. Through my work, I found that I liked coding and data analysis the most. Although my thesis topic was not related to data science, much of the work I did set the groundwork for entering a data science bootcamp: stats, data wrangling, visualizations, and presentations.
Why did you choose to learn with Springboard?
I researched lots of review sites and I ultimately went with this one because of the flexibility and excellent student reviews. I liked the human factors, like readily available advisers and coaches, a weekly session with my mentor, and lots of other avenues to reach out to another person.
What was your learning experience like?
I was learning something new every day, and I knew the material would be incredibly useful and applicable to my next stages. The self-paced structure can be stressful, and that was where interacting with my mentor and scheduling calls with Springboard coaches/advisers was helpful. It definitely challenged me and made me more comfortable taking a serious step further to prepare myself to enter another career.
I really enjoyed my calls with my mentor each week. He was very encouraging and had great feedback to provide. Even though the sessions were only 30 minutes each, I felt supported throughout the many months I was working through the bootcamp.
What was the most challenging part?
Deadlines push me to become more productive, and the self-paced curriculum didn't really have any. That was a challenge. I handled this by setting small goals that I should at minimum meet by the end of each day. It helped that I verbalized my goals to my mentor and felt somewhat more accountable as a result, but I still completed the course 1-2 months after I had planned.
What was your capstone project?
A recommendation engine of music journalists to follow, based on Spotify music preferences. I picked this project because it combined my interest in music and quantifying subjectivity in music journalism with many incredibly useful techniques and ML methods I had recently learned in the course.
Diana went on to turn that project into a Flask app that you can check out here.
What are you up to now?
I'm a machine learning engineer for a health information and clinical research company. I'm still learning and expanding my skills while also getting industry experience.
Diana and many others made the switch into data science careers, and you can too. The Data Science Career Track is a flexible, mentor-led course complete with a job guarantee.