Originally published in April 2020 and has been updated in June, 2020, September 2020, April 2021, May 2023 and January 2024 for relevancy.
After graduation, I wanted to continue working with NASA JPL; I got my PhD in Statistics from UCLA. In the process, I learned:
how to learn,
how to research,
that I need to scope down to finish my dissertation, and to
update my advisor/stakeholders on my progress more often than once every 3 years :)
which are great skills to for working in industry, especially in tech. But is it the right path for you?
I'm often asked for recommendations for the best programs for continuing education, such as graduate programs (Masters or Ph.D.), bootcamps or fellowships. My answer* is always: "it depends"... it depends on many, many factors and opportunity costs.
To help you better decide if you should work on a side project, or go to graduate school/bootcamp/fellowship and if a given program is the right fit, if/when you're considering going back to school, I recommend that you ask yourself the following questions:
Start with the end goal in mind: Where do you want to be after you finish the program? What gaps are you trying to fill (if any) with the project, degree or course work, to get you to where you want to go next?
What are the opportunity costs if do -- and don't -- go for the degree/certification?
Where are graduates of the program getting placed after graduation? Are those industries and companies you also want to work for?
Does the degree/certification align with your interests (and strengths)?
When I was getting my degrees in Statistics, I learned (many) algorithms in undergrad, the theory behind them as a Masters student, and invented my own to get a PhD.
How do you learn best?
Do you prefer self-paced: through books/videos/blogs?
Do you need accountability? And are more comfortable with instructor-based learning, through college/university/bootcamp?
Do you prefer to be project-based, by working on a project? If so, please see my tips for a side project here.
How much time can you allocate to the program, per week and in years?
Do you prefer a full-time program that's all in-person? or a is a part-time program that's after work a better fit? or something else entirely?
What is the structure of the program? What are the course requirements? Is there a capstone/thesis and/or qualifying exam requirements?
How much does it cost? Do you have to relocate?
Is there 1+ professor/faculty with the expertise that will help you reach you goal from question (1)? Who is allowed to make up your dissertation committee?
What are the requirements and application deadlines for the program?
(Bonus, if applicable) Is there any financial aid? Will your company reimburse you some/all for the tuition fees; what are those reimbursement terms?
(Bonus) Request to meet students and faculty, to hear more about their experience, tips and communication preferences.
Hope this helps! If at the end of this thought exercise you decide that graduate school is not (yet) for you, no worries! Here's more advice on what to try instead to become more marketable in the space:
Good luck!
Keywords: Graduate school, Data Science careers
You may also like:
Dear Advisor: Breaking into your First Data Role: 6 Steps to Tackling the Cold Start Problem
Dear Advisor: How to Break into your First Data Role as a Non-Analytics Manager
Should you go to Graduate School? by Alison Green of Ask a Manager
Which Data Science Bootcamp Should You Sign Up For? by Haya Toumy
Recording of panel discussion: Academia to Industry, hosted by Locally Optimistic (July 2021)
The Hustle's (hot) take that "Maybe you shouldn’t get that master’s degree in this economy" (January 2023)
Working Backwards (the Amazon Method), by Product Plan
* Irina is an advisory board member at UCLA's Masters of Applied Statistics (MAS) Program and developed and taught a graduate course for UCLA Statistics for business and software development best practices for real-time analytics/ML/AI in production
