Diana Xie

Machine Learning Engineer, IQVIA

From:   New York, NY
Course:   Data Science
Before Springboard:   Ph.D., Neuroscience
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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.

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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 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.

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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.

What was your learning experience like?

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.

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. 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.

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, while incorporating many incredibly useful techniques and ML methods I had recently learned in the course.

What are you up to now?

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.

What advice do you have for those considering online learning?

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.).

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