At The Data Incubator we run a free eight-week Data Science Fellowship Program to help our Fellows land industry jobs. We love Fellows with diverse academic backgrounds that go beyond what companies traditionally think of when hiring Data Scientists. Paul was a Fellow in our Fall 2016 cohort who landed a job with Cloudera.
Tell us about your background. How did it set you up to be a great Data Scientist?
The theme of my six years as a software engineer has been to help domain experts, whether they be fraud investigators at a bank or clinicians at a hospital, analyze disparate data to make better decisions. I have built infrastructure in both Java and Python, have used large SQL and NoSQL databases, and have spent countless hours perfecting Bash hackery (or wizardry, depending on your perspective).
My experiences as a software engineer were very relevant to data science in that I learned many ways to access, manipulate, and understand a variety of datasets from a variety of sources in a variety of formats. As the adage goes, “Garbage in. Garbage out.” No more is this true than in data science. Performing good data science requires cleaning and organizing data, and I feel very comfortable with this process.
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?
What is your favorite thing you learned at The Data Incubator?
Could you tell us about your Data Incubator Capstone project?
And lastly, tell us about your new job!
Visit our website to learn more about our offerings:
- Data Science Fellowship – a free, full-time, eight-week bootcamp program for PhD and master’s graduates looking to get hired as professional Data Scientists in New York City, Washington DC, San Francisco, and Boston.
- Hiring Data Scientists
- Corporate data science training
- Online data science courses: introductory part-time bootcamps – taught by our expert Data Scientists in residence, and based on our Fellowship curriculum – for busy professionals to boost their data science skills in their spare time.