I frequently get asked these questions about my path into data science, so I put together my common responses based on my personal opinion to help guide others!
Should I go back to school and get a Masters if I have a Bachelors?
I thought that I needed to go to graduate school to increase my data science qualifications and I did, only to drop out and leave with a certificate in analytics. I worked on a lot of open source projects to enhance my resume instead of satisfying advanced education requirements. Looking back, I did benefit from a few structured classes for learning how to program, however a full masters/PhD is not needed to land a early career data science job.
Should I go back to school and get a Ph.D?
Unless the job calls for developing novel algorithms to send humans to mars 🚀 or creating drugs to cure cancer 💉 a Ph.D is not needed for an early career data science job at most technology companies.
What if my background is different (i.e. not CS or Stats) from ‘traditional’ Data Scientists?
Maybe, It depends if your background is quantitative
A varied career background often makes for a better data scientist. I’t important to highlight the differences in background as a positive as it assists in diverse thinking on your new team. If you can’t afford to go back to school there are still plenty of online resources to teach yourself if you have the time and dedication to self learn.
How do I find a data science project?
Look to your hobbies or passions for project inspiration. I did projects centered around bioinformatics, beer and civic open data because that’s what I’m interested in.
How do I evaluate data science jobs?
When searching for jobs I look for a team that is language agnostic (R, Python, SQL, Julia), has data science tooling for predictive modeling at scale (capabilities to host shiny web servers and access to data), and mentorship opportunities for junior scientists.
Last Updated 4/24/2019