5 Things to Consider in a Data Science Master’s Degree

Getting a Bachelor’s of Data Science degree is already a huge accomplishment, but you may have realized you can’t get certain specialized jobs without more post-secondary education.

Even if you’re an experienced professional in your field, there may come a time when you’re locked out of promotions or making a higher income because you lack a master’s. Still, going back to school is an expensive, important choice that you shouldn’t take lightly.

What to Consider Before Getting a Master’s of Data Science

Whether you’re confident getting a Master’s is right for you, or you’re still in the research phase of getting a degree, here’s what you must consider before signing up for your program.

1. Remote vs. On-Campus Degree

The pandemic has made online master’s degrees more common, so if you’re situated in an area with few or no universities, you should consider completing your program through internet learning portals. Still, that doesn’t mean you shouldn’t research your school’s reputation.

When weighing the pros and cons of an online vs. on-campus master of data science, ask yourself if you can take on a full course load or move to a different location. While online degrees can be more expensive, they offer more flexibility for adults with children or jobs.

2. Tuition and Personal Finances

The average cost of tuition in the United States is $35,331 per student per year, but if you’re taking out student loans, you’re likely to pay twice that amount. In the end, you may pay $282,648 when accounting for interest, and that isn’t even counting supplies or insurance.

You’ve already spent a lot of money on your education, so you really have to consider if your Master’s degree program will get you where you want to go. Taking on an extra $60,000 in debt might be the worst decision you’ll ever make if you don’t see career longevity in your field.

3. Program Duration/Time-Spent

Like other degree programs, you can separate your Master’s of Data Science into semesters, and you’re not required to take a full course load. Not only that, but the length of a Data Science Master’s program varies greatly depending on the school and whether you’re applying for co-op.

On average, most Master’s degree programs will take 2 years, or about 33 credits, to complete. If you want to accelerate your program, you can finish a Master’s in a little under a year, but you can also decelerate and get your diploma in 3 or 4 years if you have other commitments.

4. Degree Specialization

Most students will apply for a Master’s program when they want to specialize. In data science, students can focus their studies on statistics, business analytics, machine learning, data engineering, and more, but you need to make sure your school offers the right courses.

Some universities will allow you to take general courses in data science, but these may not earn you a specialization on your master’s degree. For example, a linguistics course is excellent for a language student, but a natural language processing course is specific to data science students.

5. Capstone Project Completion

A capstone project is an optional “class” composed of multiple credits and requires you to research a single subject over two semesters. To complete a capstone, students will work with other classmates under the direction of an advisor, typically your data science professor.

Future employers love to see capstone projects on your resume because they confirm you’re an excerpt in a specific data science topic. Capstones are also closely related to real-world experience, but keep in mind you’re expected to present your project to an audience.