10 Questions to Ask Yourself when Considering a Continuing Education Program
Tips for Deciding between Project, Graduate school, Bootcamp or Fellowship to help you Land your Next Role
Post was originally published in April 2020 and has been updated in June, 2020, September 2020, and April 2021 for relevancy.
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.
TL;DR: To help you decide, start with the end goal in mind of what you want to do after you get your degree/certification and work backwards.
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?
Are you more comfortable with instructor-based learning, through college/university/bootcamp?
Do you prefer to be application-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.
Keywords: Graduate school, Data Science careers
You may also like:
Breaking into your First Data Role: 6 Steps to Tackling the Cold Start Problem
Should you go to Graduate School? by Ask a Manager Alison Green
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)
* Irina is a lecturer and advisory board member at UCLA's Masters of Applied Statistics Program.