Now do the arithmetic honestly. If the program costs $20,000 and the lost earnings are $120,000, the total investment is $140,000. If the salary rise is $18,000 a year, the payback is just under eight years. If the rise is $35,000 a year, it is four years. If you are a career changer with no current data salary, add the first job to the calculation, because that is where the biggest jump happens. Our guide to the best cloud certifications for career growth covers the cheaper route to the same job market.
When is the answer clearly no?
Four cases. If you are switching out of a low paying field with no data experience, the two year runway is risky and a portfolio plus a credential is cheaper. If your employer will not pay and you would need to borrow the full amount, the interest can eat the first two years of the payoff. If you are already a working data scientist and the program adds no new tools, you are paying for a network you may not need. And if your goal is a research or teaching role, a masters is the floor rather than the ceiling, and a PhD is what the job actually asks for.
There is a fifth case that is close. If you want a data analyst role, the degree is often overkill. Our guide to whether the Google data analytics certificate is worth it covers the analyst path at a fraction of the price, and most analysts are hired on a portfolio and SQL skill rather than a degree.
When is the answer clearly yes?
When you are already working, with or near data, and the degree changes your title, your scope, or your access to roles that require one. The degree is a filter, and filters are what stop you from being considered. If you are on a team doing analytics with a bachelor's degree and you want to lead the team, the masters is often the shortest path to being eligible for the job you are already doing.
It is also a strong yes when you are in a field where a degree is standard, such as a large bank or a research driven company, because there the credential is assumed rather than admired. The same program that is unnecessary in a small startup is what gets you past the resume filter in a large employer.
What should you do while you study?
Build in public and in parallel with the coursework. One portfolio project per term, shipped and written up, is worth more than five class projects nobody can see. Put it on a site you control, keep the code in a public repository, and write a short post about the problem you solved. That set of artifacts is what a hiring manager opens when the resume line says you have a degree. In the online data science masters worth it decision, that is what you are paying for.
Take the practical options as well. Cloud providers give free credits to students, so you can build a real pipeline on real infrastructure for nothing. If your program has a capstone with an outside partner, treat it as a job interview, because it usually is one. A partner capstone is the clearest proof in an online data science masters worth it review that the program is connected to employers.
Online or on campus?
Online wins if you are employed, because you keep your income and your network at work while you study. On campus wins if you are 22, not yet working, and want internships and a recruiter network. The degree itself is identical, because accreditation does not look at delivery mode.
Two practical points before you choose a program. Check the time to degree, because a program that allows one course per semester can take six years, and interest adds up over six years. And check the capstone, because a program with a real client project gives you the portfolio piece that the classroom version cannot. Our guide to budget laptops for online classes covers the practical side of studying for two years from home.
Photo: Goumbik, Pixabay Content License, via Pixabay (https://pixabay.com/photos/office-business-notebook-statistics-2717014/)
Fazlur Rahman
