|Course:||Foundations of Data Science|
|Before Springboard:||Senior Analyst, Warner Bros.|
This course was the first step in getting my foot in the door of the data science field. I’m grateful that Springboard was the beginning of my data science journey.
The earliest I remember hearing about data science was in 2009. I found a blog called “Flowing Data,” and after absorbing every post, I branched out to learning about Edward Tufte, R, Python, business intelligence, Tableau, and more. Five years later, one of the blogs I followed linked to Springboard’s Data Analysis Learning Path, and I learned about the Foundations of Data Science course as it was taking shape.
I had several false starts trying to teach myself R and Python because there were too many resources out there-- it was overwhelming! It’s amazing how frictionless the learning process becomes when you’re only looking at the best material out there for a specific topic, curated by folks who are industry practitioners in that field. By the end of the course, I felt fully comfortable doing a start-to-end data workflow in R.
Coming from the world of finance, I had been an enthusiastic analyzer of data and did everything I could in the sandbox that Excel gave me. What Springboard did for me was to open up the entire canvas of the internet, APIs, online interactivity, coding, analytics techniques and the whole world of data science. It has already indelibly changed my career trajectory, because I am now looking to paint on that bigger canvas.
The capstone process itself was a revelation, beginning to end.
I am the type of learner who likes to suffer alone, and I honestly wasn't sure what I would ask my mentor, Gabriela. After reading through the student guide and reviewing emails from my student advisor, I prepared for my first call, and it just took off from there. I looked at my mentor calls as my opportunity to ask Gabriela about the tips and tricks that come from extensive experience knowing the subject and being neck-deep in the field. We discussed everything from best coding practices to staying connected in the industry.
From my past experiences working on group projects in business school, I knew I wanted to pick a capstone project topic that I was very passionate about. Being the movie lover that I am, I found two APIs that would allow me to attempt to predict the opening weekend box office numbers using movie ratings and other movie metadata.
The capstone process itself was a revelation, beginning to end-- I learned several new skills and technologies, experienced the iterative nature of data gathering, and saw firsthand that sometimes the best analysis does not suggest itself until you've spent some time with the data. Your first pass isn't going to be your best one, and that was good to know. I've done more projects since, and I consistently use what I learned from the capstone to guide me through the analysis process.
I set up a consultancy and have worked with a couple of clients to provide them with analytics, business intelligence reporting, and visualizations to help them understand their business better. I am now working on a data/reporting pipeline that can automate some of this reporting.
This course was the first step in getting my foot in the door of the data science field. I hope for it to be a long, satisfying adventure as I explore the industry further and learn new technologies.
"I left academia almost two years ago, and I have no regrets."
Before : Instructor, MIT
After : Data Scientist, Wayfair
Springboard is a wonderful stepping stone to get to your goals in Data Science.
Before : Research Officer, British Columbia Government
After : Data Scientist, Boeing
Springboard helped improve the success of my company's projects.
Before : Team lead, intelligence and planning
After : Director of Performance Marketing at Nu3 GmbH