I was a research associate with the USDA Agricultural Research Service, working on conservation challenges in wetlands. I had graduated from my Ph.D. in ecology two years previous. Although I was passionate about applied ecological research, the job market was horrible. I took stock of my transferable skills (project planning, statistics, experimental design, and coding in R), and then I googled, "What jobs can I do that require statistics, analytics, and coding?" and I found data science.
I did a lot of research about data science learning options (I was a researcher, after all), and was especially interested in programs that were designed for post-academics. However, these all required a full-time commitment, and this did not work with my personal constraints. When I left my job in research, I was responsible for the bulk of the childcare for my two young children, since the change in finances required us to eliminate childcare costs. Springboard accommodated my schedule the best, without sacrificing a mentor/teacher relationship.
"I often struggled to commit enough hours each week to the program, but this was a personal limitation that I knew I would have when I began."
In general, I enjoyed the learning process and especially appreciated the mentor guidance, both for his expertise as well as the boost of external motivation. I think the capstone projects are critical to helping learners integrate the lessons with an applied, real-world example. These were critical in the job application process as well.
My mentor was especially helpful in critiquing my capstone work, and provided a great sounding board during brainstorming and troubleshooting sessions.
I often struggled to commit enough hours each week to the program, but this was a personal limitation that I knew I would have when I began. Between early morning (5 a.m. to 7 a.m.) sessions and late-evening sessions, I was eventually able to complete the curriculum. Throughout, Springboard staff were very supportive and flexible.
I completed two capstone projects, one describing patterns and predicting ridership for a DC bikeshare project, the other focused on predicting viewership of painter Bob Ross' old rerun videos on YouTube. The second project was particularly interesting and challenging, both because it stretched my statistical and machine learning skill set and required me to use the Google YouTube API. The results were not very compelling, but the skill set I used was helpful in my job search.
I am now a data scientist at a digital publishing company, where we produce mobile games and a music-video-making social app (Triller). We do everything from ETL/data engineering to BI to advanced statistics, all in a production environment. This job has been a great opportunity to explore all the niches of the data science world, and I hope to narrow down my focus while I'm in this position.
The best thing to know going into an online learning program is how you learn. This could include: What keeps you motivated? Do you learn from visuals or reading? Do you have to try it out to really “get” it? Knowing these things will help you maximize your time with an online learning program like Springboard.