Data Science Master's Degrees (Updated in 2024)

In 2024, college graduates are considering grad school as an alternative to the job market. While there has been a record-high unemployment rate for recent graduates, given the slump in big tech, there is much more to this story beneath the surface based on our state of university report.

Additional questions you likely have include: Is pursuing a Master’s degree a good idea in 2024? Which programs and universities are the best options? What can you do to boost your job prospects if you’re a new grad?

We spoke with almost two dozen Master’s graduates across Business Analytics and Data Science in the U.S., as well as gathering data from Glassdoor, Indeed, and LinkedIn to answer questions on how the current data science and analytics market is shaping up for new graduates.

Here’s what we learned:

1. The Job Market Is Tough but Recovering: While it’s always been hard to find a job as a new grad, the current recession is changing things. Openings in data science dropped 40% from May 2022 to May 2023, while FAANG and other tech giants are hiring 90% less than last year. But there is good news on the horizon; last month, big companies like Meta and Netflix started recruiting again.

2. Insights From Master’s Students: Most interviewees who graduated in 2022 secured a job after graduation. However, this year we’re seeing fewer placements from the class of 2023. We’ve also heard from some career offices how 3 and 6-month post-graduation placement rates are expected to be lower.

3. Master’s Graduates Salaries: Data science salaries reflect experience more than degree attainment. But a Master’s makes it much easier to break into the field. Master’s grads in Data Science historically make an average of $129,779, followed by Data Analytics ($127,000), and Business Analytics ($122,000).

4. Are Master’s Degrees Necessary for Data Science?: 70% of data scientists hold a Master’s degree. Among data professions, the field in which the least professionals hold a Master’s degree is data engineering (only 60%). Among data-related Master’s degrees, 48% of students pursue a Data Science degree, 27% go for a Business Analytics degree, and 26% for a Data Analytics degree. About 60% of graduates for each of these programs end up working as data scientists.

5. What You Need To Know If You’re An International Student: How do you stay in the U.S. and get a visa after graduation? Nearly half of all STEM Master’s and Doctorates in 2019 were awarded to international students, compared to only 4.6% at the undergraduate level. That preference for Master’s degrees is driven by the opportunities for securing H1-B visas for international students.

6. Student Resources: We wrap things up by providing pertinent interview question banks on topics like coding, SQL, machine learning, and case study interviews, with additional interview guides and take-home assignments for top tech companies, salary data by company and job, and learning paths.

Graduates in 2023 face a hard job market. Almost everyone entering the job market are considering other options, like grad school or jobs outside their major, says a survey by recruiting software firm iCIMS.

Unemployment for Master’s degree graduates peaks every year around June, July, or August, when most programs end. In June 2020, it was 18.9%, the highest since 2007. But then it fell to 16.7% in July 2021 and 12% in August 2022, just before the layoffs started in November.

We don’t have the 2023 peak rate yet. But if we look at April instead, it’s low: 9.1% in 2020, 2.3% in 2021, 2.9% in 2022, and 2.2% in 2023.

However, the unemployment rate is not painting the full picture. In reality, there is a demand surge for low-paying jobs and a drop in demand for high-paying tech jobs.

Entry-level data science job openings have also fallen since peak levels last summer.

From May 2022 to May 2023, job openings in data science dropped 38%. The decline steepened in the second half of 2022 but stabilized in 2023.

All data science roles were hit by this drop. Data analytics roles were less impacted, largely because they are more business-focused and linked to short-term revenue. Companies have also swapped “data scientist” titles for “data analyst” where possible, as analysts earn less on average.

The slowdown hit FAANG and big tech the hardest. They used to hire one in every 20 data scientists. Now it’s less than one in 100. That’s bad news for new grads, as these companies often pay the highest salaries in data science.

By June 2023, job openings in data science at FAANG and big tech had dropped 89.1% from the year before. For non-FAANG companies, it was a 39.3% drop.

We carried out several interviews with Master’s graduates. Despite a tough job market, most 2022 grads found work after graduation. Among other tidbits, they let us know that landing interviews at top companies have become tougher and that rejections are more common.

Despite this bad news, the majority found a job within three months of graduation. This was a common theme among those who paired their Master’s with interview practice.

These are some tips they gave us:

1. Know the type of questions the company asks. Ajay said that understanding the questions top tech firms asked gave him an edge.
2. Practice with a platform that specializes in these questions. Georgia Tech’s career center recommends Interview Query as the “LeetCode for data scientists”, according to current program participant Nicholas Gonzalez.
3. Do mock interviews with peers. Pay special attention to structuring your answers for case studies. Rohan Rajeev said watching case study videos helped him learn to approach problem-solving and investigative case studies.
4. Understand your career path and make a preparation plan. Melissa said Interview Query was “an invaluable resource for understanding the career landscape and preparing for [her] future in the field.”
5. Stay focused on your goals and track your progress. Ajay found it motivating to know the compensation for various roles, as it helped him set a target and keep practicing. Ashwani used learning paths to track his progress, saying it helped him focus, grow, and tackle tougher questions over time.

One of the hardest choices an undergraduate faces is deciding between a Master’s degree or heading straight into the workforce. We look at how salaries have changed in the past few years for Master’s degree holders.

Interestingly, one stat we found was those without a Master’s degree tend to earn slightly more at the start of their career. Their starting salaries are around $125,400, compared to $121,600 for those with a Master’s degree.

To make sense of this observation, there are two big points we need to keep in mind:

1. The sample size of non-graduates is much smaller. Non-graduates make up just 30% of professionals in data science.
2. Many Master’s students come from overseas. They often need an H-1B visa sponsorship, or they eventually find work outside the US, both of which affect average salaries.

Then, as experience grows, so does salary. For those with a Master’s degree, salaries reach up to $151,100, compared to $149,200 for non-master’s holders. As we can see, salaries are more dependent on job experience than on holding a Master’s degree. However, we will see that Master’s degrees are a great way to get your foot in the door.

Salary By Program

Master’s grads in Data Science make an average of $128,000, followed by Data Analytics ($127,000), and Business Analytics($122,000).

An M.S. in Data Science leans more technical, often steered by computer science or similar departments. On the other hand, an M.S. in Business Analytics, typically hosted by business schools, zeroes in on the business side of things.

Sometimes, schools merge their Business Analytics programs with their MBA offerings. For example, the University of Minnesota couples its MSBA with its MBA program, while UCLA offers a separate, 15-month-long MSBA.

A perk of MSBAs is their focus on job placements and strong alumni networks since they are more business-facing.

A majority of data science professionals have a Master’s degree. The percentage varies by job title. More machine learning engineers hold a Master’s degree than any other role, while data engineers are the least likely to have one.

As we can see below, the more technical the role is, the more likely it is that the professional has a Master’s degree. Data engineering is the outlier - this could be due to the specific tool knowledge required for the role.

Breaking down their highest level of education, 24% of data professionals have a Ph.D., half have a Master’s degree, 22% hold a Bachelor’s degree, and the remaining 4% haven’t graduated.

As for earnings, Master’s degree holders in data science have a median starting salary of $132,000, which can rise to $152,800 with experience.

Our LinkedIn profile analysis shows 48% of students looking at Master’s programs choose a Data Science degree, 27% opt for a Business Analytics degree, and 26% prefer a Data Analytics degree.

However, regardless of the program, around 60% of all these graduates work as data scientists.

Interestingly, a marginally higher percentage of Data Analytics Master’s graduates work as data scientists compared to Data Science Master’s graduates (67% vs. 64%).

Business Analytics follows closely, with 59% of its graduates working as data scientists.

The M.S. in Data Analytics sees the most graduates working as product managers or analysts, 3.9% in total, compared to 1.5% for Data Science and 2.5% for Business Analytics. For those aspiring to be machine learning engineers, the M.S. in Data Science leads the pack with 5.4%, leaving behind Data Analytics at 2.5% and Business Analytics at 1.1%.

What Data Science Professionals Studied As Undergrads

Computer Science also dominates at the undergraduate level for Data Scientists, followed by Electrical Engineering, Economics, Statistics, and Physics.

For a more detailed breakdown:

Undergraduate Degree	Data Science Professionals	Undergraduate Degree	Data Science Professionals
Computer Science	22.72%	Engineering	3.11%
Electrical Engineering	13.24%	Business	2.79%
Mathematics	12.82%	Finance	2.12%
Economics	9.36%	Chemical Engineering	1.64%
Statistics	5.61%	Psychology	1.59%
Physics	5.23%	Civil Engineering	1.44%
Mechanical Engineering	4.25%	Electrical	1.15%
Information Technology	3.91%	International Business	0.87%
Industrial Engineering	3.31%	Computer Engineering	0.86%
Applied Mathematics	3.13%	Management	0.85%

Master’s programs in the US see a higher influx of international students compared to bachelor’s programs. Almost half of all STEM Master’s and Doctorate degrees awarded in the US in 2019 went to international students. This is a stark contrast to the undergraduate level, where only about 4.6% of students are international. California, New York, and Massachusetts top the list with the highest numbers of international Master’s students.

There is a simple explanation for this discrepancy. To work in the US, these students usually need H-1B visas. However, the number of these visas granted each year falls short of the demand from companies wanting to sponsor new hires, and so to stay competitive international students need to hone their technical skills. This competition drives the enrollment rates in U.S.-based Master’s programs.

Our student resources include recommended interview question banks for topics like coding, SQL, machine learning, and case study interviews, with additional interview guides and take-home assignments for top tech companies, salary data by company and job, and learning paths.

These resources have been highly appreciated by our students. Here’s what some of them have to say:

Interview Question Banks

“My career advisors present Interview Query as the Leetcode for data science.” - Nicholas Gonzalez

“I went through my job search and landed a role at FedEx. Interview Query played a significant role in my success by providing me with targeted preparation materials.” - Rohan Rajeev

Interview Guides

“When I was interviewing for Amazon, the Interview Query guide was particularly helpful. The structure remained the same, so I was aware of what kind of problems would be thrown my way. This helped me be prepared and not taken by surprise.” - Anurag Mishra

Salary Data

“One feature I particularly enjoyed was the compensation information provided for various roles. It helped me set a target package and motivated me to keep practicing.” - Ajay Krishna M

Learning Paths

“My experience with Interview Query has been fantastic. One of my favorite features was the Learning Paths, which allowed me to focus on specific topics and track my progress.” - Ashwani Varshney

“With Interview Query, I found exactly what I needed to prepare for interviews, focusing only on the essential topics. Their learning paths, like the one tailored for Amazon, were incredibly helpful in understanding the interview process.” - Samarth Patil

Take-Home Assignments

“Seeing other people’s code, getting access to answers, learning about the different career paths using analytics.” - Melissa Chi

Other Insights

“What sets Interview Query apart is its focus on real-life, practical scenarios in the questions, which truly prepares you for the actual job role.” - Ajay Krishna M

“Overall, Interview Query has been an essential tool in my data science interview preparation, and I attribute much of my success in landing a job to this platform.” - Nicholas Gonzalez

“I found it beneficial in strengthening my weaker areas. It has given me exposure to various problem sets and allowed me to practice and refine my skills in SQL, Python, and other aspects of data science.” - Nicholas Gonzalez

Overall, our resources and platform have helped many students succeed in their data science journey. We hope to continue serving our students and providing them with the tools and resources they need to excel in their careers.