Diana Xie had just earned a master’s degree in neuroscience from an Ivy League university and was on the Ph.D. track when she decided that she wanted to change careers.
Although her thesis topic was not related to data science, she found herself picking up skills like data wrangling, visualizations, and presentations. She realized she loved coding and data analysis.
“Much of the work I did set the groundwork for entering a data science bootcamp,” she said.
But which one was right for her?
“I researched lots of review sites,” Diana said, and she ultimately chose Springboard’s Data Science Career Track “because of the flexibility and excellent student reviews. I liked that there was a human factor, which was readily available advisers and coaches, a weekly session with my mentor, and lots of other avenues to reach out to another person.”
That human element was particularly important as she adapted to the self-paced structure of the course.
“Deadlines push me to become more productive, and the self-paced curriculum didn’t really have any. That was a challenge,” she said. “It helped that I verbalized my goals to my mentor and felt somewhat more accountable as a result.”
The weekly mentor calls were a highlight, she said. “Even though the sessions were 30 minutes each, I felt supported and helped along the many months I was working through the bootcamp.”
One of the things she liked most about the Springboard experience was that she was learning something new every day. And what she was learning was directly applicable to her career goal.
As part of the course’s hands-on learning, Diana built a recommendation engine of music journalists for consumers to follow, based on their Spotify music preferences.
“I picked this project because it combined my interest in music and quantifying subjectivity in music journalism, while incorporating many incredibly useful techniques and ML methods I had recently learned in the course,” she said.
Diana went on to turn that project into a Flask app.
After completing the course, Diana got a job as a machine learning engineer for a health information and clinical research company.
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.
I researched lots of review sites and I ultimately went with this one because of the flexibility and excellent student reviews. I liked that there was a human factor, which was readily available advisers and coaches, a weekly session with my mentor, and lots of other avenues to reach out to another person.
"Consider what motivates you and your style of learning, especially with the self-paced nature of this course. Preparation for calls will also be useful for getting the most out of the bootcamp."
I was learning something new every day, and furthermore I knew that it 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 not just casually self-learning with the help of others, but taking it a serious step further 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 30 minutes each, I felt supported and helped along the many months I was working through the bootcamp.
Deadlines push me to become more productive, and the self-paced curriculum didn't really have any. That was a challenge. 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. I overcame this by setting small goals that I should at minimum meet by the end of each day.
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, while incorporating many incredibly useful techniques and ML methods I had recently learned in the course.
I'm a machine learning engineer for a health information and clinical research company. I'm still learning, expanding my skills, and now getting industry experience.
Consider what motivates you and your style of learning, especially with the self-paced nature of this course. Preparation for calls will also be useful for getting the most out of the bootcamp. Take advantage of all the resources, but also go beyond that and network/prep for interviews (Google interview questions and how to prepare, etc.).
