computer screen with lines of code

Is a Master’s in Data Analytics Worth It? What You Need to Know to Make Your Decision

With an average salary of $82,360 and a US job market poised to grow by 23% over the next ten years, data analytics is an attractive career path, especially when compared with the average $58,260 salary and 5% job market growth for the US as a whole. To keep pace with the rising demand for data analysts, business analysts, and data scientists, colleges and universities are increasingly offering online and in-person master’s degrees in data analytics as a way for individuals to break into the field or get ahead in their careers. But is a master’s in data analytics worth it? The answer’s not necessarily as clear as the numbers would suggest. 

Data analytics master’s programs provide expert-led training in data management, analysis, and visualization — as well as core computer science skills like programming — but the expense, prerequisites, duration, and career prospects of these programs can mean that they don’t make sense for everyone.

In this article, we’ll break down the associated costs and potential outcomes of a data analytics master’s program to help you determine for yourself if it’s the best next destination on your career path. If you decide it’s not, we’ll also include some ideas for other options that might be a better fit. 

First, however, we’ll dive into the basics: what is data analytics, what does a master’s in data analytics entail, and what are some of the reasons for pursuing one?

What is data analytics?

Data analytics centers on analyzing data with statistical methods to extract valuable insights, but it’s much more than that. In addition to analysis, data analytics professionals must be skilled in the processes required to collect, organize, prepare, and store the data they work with. They also need to be able to compellingly present their findings.

There are four primary kinds of data analytics: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics, each answering a fundamental question using data.

  • Descriptive analytics answers the question: What happened in the past?

  • Diagnostic analytics answers the question: Why did something happen in the past?

  • Predictive analytics answers the question: What might happen in the future?

  • Prescriptive analytics answers the question: What actions should be taken?

For more on the different kinds of data analytics plus some real-world examples, check out our data analytics explainer.

What does a master’s in data analytics entail?

A master’s in data analytics is a one-to-two year graduate degree that aims to equip students with the theoretical and practical knowledge and training that will allow them to pursue data analytics careers. Generally students are required to take a number of core courses and may then choose electives according to their interests or specific concentration tracks. In some cases, students will complete an internship, practicum, or capstone project before they graduate in order to put what they’ve learned into practice and demonstrate their job-readiness.

What core topics does a master’s in data analytics cover?

Each program is different, but typically data analytics master’s students can expect to cover the following over the course of their graduate program:

  • Computer science skills, such as programming with languages like SQL, Python, or R. For more on data analytics and data science programming languages, see our deep dive.

  • Statistical methods for data analysis, such as optimization and regression, as well as fundamental machine learning techniques

  • Data management, including data collection, data preparation, data mining for big data, and data storage

  • Data visualization practices and tools (charts, graphs, heatmaps, dashboards), usually with a software like Tableau

  • Use-cases for data analytics in business, non-profits, or the public sector

What are the different ways you can attend a master’s in data analytics program?

While on-campus instruction used to be the only option for students looking for graduate-level instruction in data analytics, online master’s degrees continue to gain market share. According to data provided by Urban Institute and published in Inside Higher Ed, there was nearly 10% growth in the amount of master’s students studying online between 2012 and 2016. With universities’ reactions to COVID-19 offering many prospective students an important proof-of-concept for online study, this growth will no doubt continue in the future. Increasingly, students are also pursuing data analytics master’s programs part-time, either on campus or online. Here are the advantages and disadvantages of each:

On campus

Tried and true, an on-campus experience lets you interact (and network) with your peers and your professors face-to-face and make use of campus facilities like libraries and student centers. You can also take part in various student interest groups and attend enrichment events. But on-campus study does have some drawbacks: students often have to relocate and find housing or at least commute, both of which can be expensive and time-intensive.


By allowing students to study from the comfort of their own homes (or wherever else they wish), online study offers them the flexibility to continue to work, take care of family obligations, or maintain their hobbies. Often, this has a financial upside, whether through housing and travel savings or avoiding potential loss of income. For more on online data analytics master’s program, see our guide.


Full-time study offers the fastest path to a master’s degree, and so the fastest path to the next step on a student’s career path. But this comes at a cost: while students can still study full-time online, doing so can make it difficult to continue working. The same is true for on-campus study: if pursued full-time, it can be difficult to focus on anything else, which can bring financial downsides.


Whether in person or online, part-time study can offer flexibility while usually extending the time-to-degree. As with online study, if a student wishes to continue working, this is usually no problem. Without money coming in, however, part-time study can end up being less efficient and more costly.

Who should pursue a master’s in data analytics?

We’ve just covered what a master’s degree in data analytics entails and gone over options that can give a wide variety of students the opportunity to pursue master’s study. Let’s dig deeper now into who exactly could benefit from graduate study in data analytics. 

Generally speaking, data analytics master’s degrees are tools for 1) career advancement and 2) career transition. We’ll start with career advancement.

Career advancement

Many who seek out data analytics master’s programs are already active in the field and looking to improve their skills and learn field-leading practices to advance their careers, either at their current company or elsewhere. Oftentimes, a master’s degree is a requirement for more senior roles, so completing a program can help ensure a candidate’s resume grabs the hiring manager’s attention.

Career transition

In addition to those looking to advance their careers are those looking to transition careers. For someone with a bachelor’s degree in STEM, the social sciences, or even the humanities, a data analytics master’s degree can not only provide the skills and experience needed to work as a data analyst, but also assure a hiring manager that a candidate from a non-traditional background is job-ready.

What are the prerequisites for graduate study in data analytics?

The above use-cases show that graduate study in data analytics is open to aspiring data analysts from a variety of backgrounds, but there are often still prerequisites to get admitted. Across the board, programs require applicants to hold a bachelor’s degree at the time of matriculation, provide their GPA and transcripts, and, for international students, demonstrate English proficiency. Many programs also require applicants to submit GRE scores, though some are loosening these requirements.

When it comes to prior experience, programs differ widely. Some demand applicants have at least rudimentary skills in statistical analysis or programming, others want applicants to arrive with work experience, and still others welcome complete newbies, even providing pre-program bootcamps to get them up to speed. 

Is a data analytics master’s degree worth it for you?

We’ve covered what a data analytics master’s is and who can benefit — but how can you know if it will be worth it in your particular situation? Best practice, in our mind, is to complete a simple cost-benefit analysis: what are the costs associated with a data analytics master’s degree, what are the potential outcomes, and how likely are these outcomes? We’ll cover each in detail.

What are the costs associated with data analytics master’s degrees?


Higher education isn’t cheap, and data analytics programs are no exception. According to, the typical master’s of science degree in the US costs $61,200, but the cost of the degree can range from $30,000 to $120,000 once fees are factored in.

Oftentimes, master’s degrees from public universities are significantly more affordable — averaging $29,150 — especially if a graduate student can take advantage of in-state tuition discounts.

When considering tuition costs, keep in mind that some employers offer tuition assistance for employees that wish to return to school to upskill. You might also qualify for private scholarships or federal grants that could significantly defray the cost of school.

Temporary Relocation, Housing & Travel Costs

For on-campus programs, you should also factor in the cost of commuting if you attend a nearby program, or relocation and housing costs if you will need to move away to attend school. One advantage of online programs, as we mentioned above, is the avoidance of these kinds of costs.

Lost potential income and/or other expenses

Most master’s programs are one to two years if you attend full-time. If you are currently working or have familial obligations and want to keep your length of study short, this means one to two years that you won’t be pulling a salary or might need to pay for another caregiver or babysitter. This can be avoided if you opt for online and/or part-time study, but keep in mind that this might also extend the duration of your course of study.

Student loan interest

Many who choose to pursue master’s degrees decide to take out loans to finance their education. In fact, according to, “[t]oday’s graduate students are 2.5 times more likely to borrow for school than graduate students in 1995,” with average debt increasing twofold over the same timeframe to $88,212. If you are considering taking out loans to pay for a master’s in data analytics, be clear on how much you will have to pay back in interest and principal and if this will be feasible given the career prospects.

What are the potential outcomes of a data analytics master’s degree?

If you focus just on the costs associated with a master’s degree, it can look pretty dire, but graduating with a master’s degree in data analytics from a reputable program means that you will potentially have lucrative career prospects. Many graduates go on to be data analysts, business analysts, business intelligence analysts, and, in some cases, data scientists. Here’s how these positions differ:

Data analyst

A data analyst gathers and prepares data for analysis and analyzes it to extract insights related to business operations, product strategy or performance, or some other initiative. Often, the analyst will create data visualizations to assist in communicating their findings to relevant stakeholders. estimates the average data analyst salary in the US to be $81,719.

Business analyst

A business analyst collects, prepares, and analyzes data to yield business insight to inform future business actions. estimates the average business analyst salary in the US to be $79,770. You can learn more about the difference between a data analyst and business analyst in our explainer on the topic.

Business intelligence analyst

A business intelligence analyst’s work is similar to that of a business analyst, but instead of analyzing data to help inform future actions, their focus is on producing informational reports and dashboards on markets, industries, or business performance. estimates the average business intelligence analyst salary in the US to be $85,278.

Data scientist

Like a data analyst, a data scientist collects, prepares, and analyzes data, but a data scientist is also responsible for designing, developing, and deploying new approaches to data analysis, often through machine learning. Often, many of the tools and techniques a data analyst employs on a daily basis will have been first created by a data scientist. For more on the differences between a data analyst and a data scientist, head to our article on the topic. estimates the average data scientist salary in the US to be $139,631. While some with data analytics master’s degrees land in data science jobs, if you’re looking to become a data scientist, you should also check out our guide on data science master’s programs. Further below, we’ll discuss how to decide between the two.

How likely are the potential outcomes of a data analytics master’s degree?

Given just the associated costs and the potential outcomes, a data analytics master’s degree seems it would offer sufficient return on investment. Though the upfront costs can be substantial, as can student loan interest, the degree can put students on a well-paying career path or move them down one they’ve already started. With a master’s degree, there will likely be more opportunities for advancement that can push compensation to well over the average salaries listed. With time, the initial investment would pale in comparison to lifetime earnings. This is especially the case if you take advantage of a part-time or online program that could allow you to study from home and continue working while you do so.

But while these potential outcomes are appealing, it’s always important to keep in mind how likely these outcomes are. Unfortunately, this can be difficult to do, not only because student outcome data can be hard to come by, but also because so much depends on the aspiring data analyst in question. So what can you do to start determining the likelihood of success? Start here:

Seek out hard data

Some programs do list student outcomes, such as the University of Chicago, which boasts that 75% of its students find a new job while pursuing their Master’s in Analytics degree. We keep a running tab of student outcomes for programs that publish this data, which you should make sure to utilize as you are making your decision. Knowing the hard facts of where graduates of certain programs end up in the aggregate is invaluable in determining how likely your investment will be rewarded.

Do guerilla research

For programs that don’t list outcome data — and even those that do —, you should go guerilla: leverage social networks like LinkedIn to find graduates of programs that interest you and figure out where they ended up. You might consider seeing if they would be up for a quick chat so you can ask them what they thought of the program and how much they think it contributed to their success.

Perform a self-audit

Once you get a better idea about the kinds of people entering these programs and the kinds of outcomes they are having, you can begin looking inward. How do you compare to the graduates you researched or spoke to? Do you have a similar background, or similar aptitude for quantitative study? Do you have sufficient drive and interest to commit to a master’s program? Are there professional networks you can tap into that could make your job search easier? Think also about what they said about the programs: did they provide a good foundation that could be leveraged to begin a data analytics career or move up in the field?

Performing these steps can help you determine your confidence level as to the likelihood that master’s-level study could help you find employment in data analytics. Once you do this, you can begin weighing the costs and benefits in earnest: plotting out the financial impact of the degree in the near- and mid-term, the financial payoff in the mid- and long-term, and, ultimately, the feasibility of pursuing such a course of study given your current financial situation. As you do so, you should also consider alternative educational pathways that could ultimately get you to the same place. In the next section, we’ll dive into these.

What are some alternatives to a data analytics master’s degree?

While a data analytics master’s degree can be a great way to move down a data analytics career path, it’s not the only way. As you decide whether a master’s in data analytics is worth it for you, consider the following options that can provide cost and time savings, more desirable outcomes, or both.

Business analytics master’s degree

Pursuing a master’s degree in business analytics won’t necessary change your financial calculus — a business analyst and a data analyst earn roughly equivalent salaries on average — but it might open up more interesting career opportunities. If you’re interested in a more business-oriented curriculum that can give you the business acumen to apply your analytics skills to big-picture questions about maximizing revenue, reducing costs, entering a new market, pursuing mergers and acquisitions, etc., head over to our business analytics master’s guide to learn more about how these programs differ from data analytics master’s.

Data science master’s degree

If you are confident in your aptitude for quantitative analysis and computer science and are interested in working extensively with machine learning, you might consider pursuing a master’s degree in data science instead of data analytics. Generally these programs are more demanding, but, as you saw above, they also offer more lucrative outcomes, with six-figure salaries being the norm. This could significantly change the financial calculus of whether to pursue a graduate degree — so it’s worthwhile to look into it. Head to our guide on data science master’s to learn more about the degree and get our recommendations for the best programs out there.

Data analytics or data science bootcamp

Increasingly a go-to option for individuals looking to advance their careers without spending the time and money on a bachelor’s or master’s degree, bootcamps are months-long, intensive courses of study solely to prepare participants for entry-level jobs in a particular field.

Accordingly, bootcamps focus on practical, job-ready skills and place less emphasis on the theoretical background that traditional degree-programs would provide. Oftentimes, they include a capstone project, which allows students to apply what they have learned independently and begin a portfolio to demonstrate their skills to potential employers, as well as substantial careers-guidance.

While tuition varies, bootcamp attendees can generally expect to pay between $10,000 and $18,000 for their education; the average bootcamp cost $11,727 in 2020. This is significantly less than the cost of a master’s degree, but keep in mind that your career prospects and average salary will generally be better if you hold a master’s, at least at first. 

If you want to learn more, check out our guides for data science bootcamps (and online) and data analytics bootcamps. We’ve also written up a similar examination of the costs and benefits of a data science bootcamp that you might find helpful.

Data analytics short courses

Master’s degrees and bootcamps provide comprehensive training in a broad set of skills, but what if you’re looking to just improve one aspect of your data analytics repertoire? In this case, you might save time and money by taking a data analytics short course, certificate, or certification program. While some of these programs do offer a broader overview, many are more targeted, focusing on a particular software or skill. And while master’s degrees and bootcamps cost in the tens of thousands of dollars, for the most part these programs are at worst a couple of thousands of dollars, and in the best cases, completely free. 

If, in reading about data analytics master’s programs, you’ve become convinced that the financials don’t make sense for you, they’re too time-intensive, or the curriculum might be overkill for your purposes, head to our data analytics short course guide to learn more about what you can expect from one and see our favorites.

A data analytics master’s is worth it for you: what’s next?

Over the course of this guide, we’ve covered the basics of data analytics, dived into what a data analytics master’s program entails, and examined the reasons more people than ever are entering these programs. We’ve also explored the costs associated with data analytics master’s, the potential outcomes and their likelihoods, plus alternative ways to break into data analytics and data science.

If, in reading all this, you’ve decided that earning a graduate degree in data analytics is the right next move for your career, what should you do next? The first step should be to check out our full guide on data science master’s so that you can get a fuller overview of the degree and see our tips for finding a program that would work for you. While you’re there, you can also check out some of our favorite programs in case one interests you. If you’re interested in the flexibility and potential savings of online study, you should also check out our guide on online data analytics master’s programs.

If you’re new to data analytics, it would also be a good idea to get smart on what to expect from a data analyst career path: how much you can expect to earn in entry-level, mid-level, and senior positions and the education, qualifications, and skills companies expect you to have at each step of the way. In this article, you’ll also see some real-world job postings so that you can start to get an idea of what the day-to-day of a data analyst might look like. If you’re looking for some tips and tools that can help you speed along your transition into data analytics, you can find them in our step-by-step guide to breaking into data analytics.