Data Science Career Track
Peter Liu
Before Springboard:
Scientist at Sandia National Laboratories
After Springboard:
Business Intelligence Analyst at Indeed
Working with my mentor was like discussing work progress with friends or colleagues.
Working with my mentor was like discussing work progress with friends or colleagues.
What were you doing before Springboard?

Research scientist in a national lab focusing on analytical chemistry. My work was related to data/spectrum analysis, and I wanted to learn data science and interview skills to broaden my career path.

Why did you choose to learn with Springboard?

Six month money-back guarantee. Also, I could pay by month. If I could finish the course before six months, I could end up paying much less money.

"He gave me suggestions as well as challenges during my project. Also, the help from career coaches benefited me a lot."

What was your learning experience like?

Wonderful. Especially collaborating with my mentor, who led me through starting projects into deploying, which is extremely helpful during the interview process. Working with my mentor was like friends or colleagues discussing work progress. He gave me suggestions as well as challenges during my project. Also, the help from career coaches benefited me a lot.

What was the most challenging part?

My challenge was machine learning. I had to read through other materials in addition to course materials.

What was your capstone project?

Machine learning application in early cancer detection: I chose this project because 1) it relates to my chemistry background and 2) it is an eye-catching topic, which earns me lots of interview opportunities. I built a web app where you could tell the probability of having cancer by uploading a spectra of blood samples.

What are you up to now?

I am a business intelligence analyst at Indeed.

What advice do you have for those considering online learning?

Work closely with your mentor. Build a readily deployable app using machine learning models to solve interesting problems that could make high impact. Present your work in front of experts and receive comments and critiques. Be social and join local data science network.

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