Newton was a Fellow in our Summer 2016 cohort who landed a job with one of our hiring partners, Crunchbase.
Tell us about your background. How did it set you up to be a great Data Scientist?
What do you think you got out of The Data Incubator?
What advice would you give to someone who is applying for The Data Incubator, particularly someone with your background?
Start thinking of problems you can solve with data now. Start learning Python and exploring the wealth of modules available for it. You can install Jupyter notebook on your local machine, which will help you play around with scraping and machine learning. The two months go by really fast, and anything you can do ahead of time will help you tremendously.
What is your favorite thing you learned at The Data Incubator?
HackerRank challenges are fun, and practicing them helped me nail the technical challenges I was given in interviews, impress the interviewers, and land the position. The Data Incubator provided solutions were always clever and elegantly written, which really helped me think of algorithms from different angles. In fact, one of the interviews actually used HackerRank to conduct the interview, so being familiar with the format and interface really helped.
Could you tell us about your Data Incubator Capstone project?
Rate to Plate recommends recipes and restaurants from a user’s ratings of restaurants. It first generates a restaurant’s flavor profile using a TF-IDF analysis of the Yelp review text with focus on key flavor-indicating terms, which I scraped from a pretty comprehensive list of food terms on BBC Food. Using a user’s rating of restaurants, the user’s flavor profile is obtained by aggregating the restaurant flavor profiles weighted by ratings. This profile is then matched with other restaurants and recipes that I scraped from Epicurious. The bulk of the data is from the Yelp academic dataset, which I supplemented by implementing a live-scrape feature on my app.