Data science master’s programs are a relatively new phenomenon, with most launching in the last five to seven years. These programs have gained ground on traditional master’s in statistics or computer science, which many data scientists pursued before full data science programs were offered.
No matter what subject you study, one thing remains true: A master’s degree results in a significant salary bump. In fact, the average master’s in data science salary is $129,592.
While a master’s degree isn’t required for a data science career, it can significantly increase your career earnings. We took a closer look to compare data science bachelor’s vs. master’s salaries and provided some information about weighing pros and cons.
At all career levels, a master’s degree results in a salary increase. Yet, the wage gap increases significantly among entry-level to mid-career data scientists. Here’s a comparison of bachelor’s and master’s degree in data science salaries depending on the career level:
Another option is a PhD. For example, a PhD in Economics, a PhD in Data Science, or a PhD in Statistics all provide training for data science research jobs with high starting salaries.
One question we hear a lot is: Do I need a master’s degree in data science? The short answer is that if you are planning on pursuing a career in data science, you don’t necessarily need a master’s. It’s also important to note that it’s not a golden ticket to a data science job.
Rather, a master’s is a good investment if:
The big consideration is cost. In-person master’s programs cost an average of $45,000 and require 1-3 years of study. Even online programs run an average of $30,000. If you have the time and resources to invest, a graduate degree is an effective tool for helping you advance in your career. Plus, along with the salary increase, graduate programs also offer benefits like:
1. Domain Expertise - You’ll be able to dive deep into foundational data science concepts, like statistics, machine learning, and probability. Plus, most programs allow you to specialize and gain experience in advanced subjects like NLP, text mining, deep learning, or computer vision.
2. Portfolio Work - Most data science master’s programs encourage project-based learning, competing in Kaggle competitions, and developing a data science portfolio. This can be a powerful tool for helping you land a competitive job.
3. Networking - The best data science master’s programs have established career networks and can help connect you with alumni working in the field. These references can help to fast-track you into landing interviews.
Should you get a master’s in data science? Our founder Jay took a closer look in the video below, and he answered questions you might have about the reasons why to pursue a data science master’s.
Specifically, a master’s is a good idea if you are considering a dramatic career switch, if you want to learn the fundamentals of data science, or if you’re a good student and prefer structured learning.
Yet, there are pros and cons to pursuing a degree, and a master’s doesn’t guarantee you actually land a job. Check out the video for more more specifics. It can help you understand if a master’s program is right for you:
A data science master’s degree will certainly impact your career earnings. However, although a master’s degree is becoming an increasingly required qualification (or preferred qualification, at a minimum) for many DS jobs, you must still have:
Master’s programs can help you to build your portfolio and professional network, and many programs also offer career counseling to help you land jobs. Ultimately, it comes down to your ability to ace the interview.
Check out these resources from Interview Query to brush up on your data science skills and see the most common interview questions: Data Science Course, Machine Learning Course, 500+ Data science interview questions.