Data Science

Nine Things Top Master’s in Data Science Programs Have in Common

Nine Things Top Master’s in Data Science Programs Have in Common
You can't just wake up one morning and decide that you're now a data scientist. Data science jobs require years of training and experience acquired through a mix of academic and professional learning. Image from Pexels
Tom Meltzer profile
Tom Meltzer January 31, 2021

What automobiles were to the 1920s and PCs were to the 1990s, Big Data is to the 2010s and 2020s. If you're interested in working in this burgeoning field, you'll want to consider pursuing a master's in data science. Here's what you can expect.

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By now, you may have heard that we live in the age of Big Data. It’s been in all the papers. Also, many of us perform work driven—at least in part—by the avalanche of new information for which our era is named.

If Big Data isn’t the growth industry of the early 21st century, it’s certainly near the top of the list. Glassdoor named data scientist the second-best job in America for 2021 (Java developer eked out the first-place finish), citing its high levels of job satisfaction, excellent median base salary (over $110,000), and large number of open positions in the field.

Problem is, you can’t just wake up one morning and decide that you’re now a data scientist. Data science jobs require years of training and experience acquired through a mix of academic and professional learning. A staggering 90 percent of all data scientists hold a graduate degree; nearly half have a PhD. Not all are technically data science degrees; because this field combines mastery of so many disciplines, it’s possible to work in data science with a degree in statistics, mathematics, engineering, business administration… or data science. As the discipline expands, more professionals are seeking data-science specific degrees.

If you’re thinking about earning a master’s in data science, you’re probably wondering what you can expect from the program you attend. Programs differ based on faculty areas of expertise, demands of the local job market, and the division of the university offering the degree (Is it an engineering degree? A math degree? A business degree? You’ll find examples of each).

Certain elements, however, are fairly consistent across all programs. We’ve identified nine things master’s in data science programs have in common. We list them below, along with the answers to these questions:

  • What is data science?
  • What is a master’s in data science?
  • What do all top master’s in data science programs have in common?
  • Top MSDS programs
  • Top online MSDS programs
  • Least expensive MSDS programs
  • Least expensive online MSDS programs

What is data science?

Most experts agree that data science is an interdisciplinary subfield of computer science. Its hybrid nature makes it difficult to define, and as a result there is no single agreed-upon definition of the term. Some folks aren’t even sure whether it’s technically a science.

Definition of data science

Data science involves the application of Big Data tools, software engineering, statistical methods, and mathematical theory to find patterns in massive data sets. These patterns often yield valuable insights about past performance and, perhaps more importantly, likely future events.

It’s a field that covers a lot of ground. As Dr. Ganapathi Pulipaka, Chief Data Scientist at Accenture, explains, data science is a field that blends “software engineering, predictive analytics, machine learning, deep learning, HPC, supercomputing, mathematics, data mining, databases (SQL, NoSQL), Hadoop, streaming analytics platforms for live analysis (Apache Kafka, Apache Flink, Apache Spark, Apache Impala), IoT platforms, edge computing, fog computing, networks, statistics, web development, cloud computing, data engineering, and data visualization.”

Data science specializations

Data science can be applied pretty much anywhere there’s enough data to study. Businesses use it to disaggregate and analyze markets, predict economic trends, and optimize operations. Health sciences use data science to improve the accuracy of diagnoses, drive research for cures to diseases and conditions, and track public health trends to stop the spread of contagions. Netflix and Amazon use it to drive customer recommendations. Web developers use it to speculate what features will push their content to the top of Google’s search results.

Data science specialization areas include:


“I’m Interested in Data Science!”

Data science professionals can use their knowledge and skills in many ways and in almost every industry. You might specialize in business intelligence or robotics or healthcare informatics. There are almost too many options.

90 percent of data scientists hold master’s degrees, and 47 percent hold doctoral degrees. (source)

The Bureau of Labor Statistics sets median data scientist annual pay at just over $100,000. Top-paying sectors include (source):

- Computer and peripheral equipment manufacturing ($148,290)
- Semiconductor and other electronic equipment manufacturing ($142,150)
- Specialized information services ($139,600)
- Data processing, hosting, and related services ($126,160)
- Accounting, tax preparation, bookkeeping, payroll services ($124,440)

University and Program Name Learn More

What is a master’s in data science?

You don’t need a graduate degree to work in some fields. Even some computer science jobs are open to those whose highest degree is a bachelor’s degree. However, in data science, the vast majority of professionals hold at least a master’s degree. According to BurtchWorks, 90 percent of all data scientists have completed graduate programs. 49 percent top out at the master’s; nearly as many (41 percent) hold a PhD.

A master’s program in data science explores the many disciplines that constitute the field and the ways they interact. Undergraduate degrees in data science are relatively rare, so few master’s-level students arrive with a deep academic background in the field. That doesn’t mean they are neophytes. On the contrary, most have studied mathematics, engineering, computer science, and statistics at the undergraduate level. Nearly all also have several years of post-undergraduate professional experience. Most master’s programs require it, in fact.

It’s worth noting that, because data science is a multidisciplinary field, you do not necessarily need to earn your master’s in data science to become a data scientist. A data-focused master’s in mathematics, statistics, computer science, business administration, or another related field will qualify you for many data science job opportunities.


The data science master’s admission process is competitive. Most admissions committees look for most or all of the following qualifications:

  • Strong undergraduate GPA
  • Good standardized test scores (most schools accept the GRE)
  • Evidence of relevant professional experience
  • Compelling essays and letters of recommendation


Core courses and electives vary from program to program, of course, but most programs cover a common set of basics. Consider the curriculum at the University of Virginia ‘s online Master of Science in Data Science program, which includes:

  • Bayesian Machine Learning
  • Big Data Analytics
  • Data Mining
  • Ethics of Big Data
  • Exploratory Text Analytics
  • Foundations of Computer Science
  • Linear Models for Data Science
  • Machine Learning
  • Practice and Application of Data Science
  • Programming and Systems for Data Science

Do you need a doctorate?

You don’t need a doctorate, but an awful lot of the data scientists with whom you’ll be competing for jobs have one. This is the rare field in which PhDs don’t necessarily funnel primarily into academic positions. Major tech employers, such as Google, pride themselves on the number of data science PhDs they hire.

Should you decide to pursue this degree, expect to commit at least four years to it. Not all of that will require a full-time commitment. You’ll begin the program completing coursework, then move onto a research and teaching phase. The bulk of your time will be committed to your research project, which you will ultimately defend before a panel of experts to earn your degree. Many programs also require PhDs to pass a comprehensive qualifying examination.

What do top master’s in data science programs have in common?

Data science is a relatively new field, and academia is still figuring out exactly what it is that someone academically trained as a data scientist should know. That said, particular traits recur in most of the top programs. We’ve listed them below.

Data science students should have professional experience

Some schools require applicants to have several years of professional or research experience in data science, data analytics, data engineering, applied statistics, business analytics, or business intelligence. All top schools prefer experienced students. The goal is to create a learning environment in which students can learn as much from one another as they can from instructors.

Data science students must know at least one programming language

Top data science master’s programs look for students proficient in at least one of the following:

  • C++
  • Java
  • Python
  • SQL

In addition, they prefer students who know how to use frameworks like Apache, Hadoop, Hive, and Mahout; programming language interfaces like Jupyter Notebooks; and data visualization tools like D3.js and Tableau.

There are programs that will teach you all of these, but the most prestigious programs expect you to arrive knowing at least some of them, and able to demonstrate proficiency in several.

Common coursework

Most master’s in data science programs require 30 to 36 course credit hours, which translates to 10 to 12 courses. Although coursework varies from program to program, you can expect your master’s program to cover most if not all of the following:

  • Algorithms
  • Applied statistics and experimental design
  • Business analytics
  • Business intelligence
  • Computer systems
  • Cyber security
  • Data analysis
  • Data management
  • Data mining
  • Data visualization
  • Data warehousing
  • Ethics and the law
  • Human-centered data science
  • Machine learning and artificial intelligence
  • Massive data storage
  • Probability theory
  • Software design
  • Statistics

Research/practicum/capstone project

Data science master’s programs typically culminate in a terminal project: either a research paper, a practicum that places students in real-world settings, or a capstone project. The University of Virginia online MSDS groups students in teams of two to four for a two-semester capstone project. Recent projects have included:

  • Improving Credit Fraud Protection
  • Preventing Fatal Motor Vehicle Accidents
  • Understanding Public Attitudes Toward COVID-19 with Twitter
  • Using AI to Diagnose Disease


Data science has many different applications to many different fields. That’s why data science master’s programs typically offer areas of specialization, also called concentrations. Suppose you want to become a data scientist in healthcare. You’ll need specialized knowledge about the practices and business of medicine; you will likely specialize in healthcare data.

Most data science master’s programs offer concentrations in some or all of the following areas:

  • Big Data analytics
  • Business analytics
  • Computational methods
  • Data acquisition and management
  • Data security
  • Healthcare
  • Hospitality
  • Machine learning
  • Mathematics
  • Marketing
  • Natural language processing
  • Physics
  • Statistical practice

They take a full-time student one to two years to complete

Depending on their previous coursework and professional experience, full-time students can complete some master’s of data science programs in as little as one year. In fact, the online program at University of Virginia can be completed in eleven months. Many students take two years to complete the degree, and part-time students, of course, take longer: three to four years, typically.

They are offered by engineering programs or computer science departments

Data science blends inputs from numerous disciplines (applied mathematics, statistics, computer science, engineering, business administration, health sciences) but for now, most of the heavy hitter programs are housed in university engineering and computer science departments. Computers still do most of the heavy lifting in data science, after all, and the comp sci and engineering programs at most universities are their most computer-proficient.

They provide a significant career boost

According to LaborInsight, master’s-level data scientists do not earn substantially more than bachelor’s-level data scientists. The employment-data aggregator sets the average data scientist salary at $113,3000 for those with a bachelor’s degree, and $116,800 for those with a master’s.

This data is misleading, however, because of the evolution of data science. The recent trend has been toward requiring a master’s or even a doctorate to qualify for the best data science jobs. As a result, most bachelor’s-level data scientists in the data pool are mid- or late-career professionals. This is what accounts for their relatively high salary compared to the master’s pool, which is, on the whole, younger, with fewer years of professional experience. Going forward, expect the disparity between these two averages to increase significantly. And, expect fewer roles to be open to those who lack a graduate degree.

They also offer PhDs

With over 40 percent of working data scientists holding a PhD, this is an exceptionally popular doctorate. Most schools with a reputable data science program and a graduate program (the Venn diagram of those two groups is pretty much a circle) also offer a PhD.

Top MSDS programs

Top Master of Science in Data Science programs include:

Top online MSDS programs (100)

Schools offering top online Master of Science in Data Science programs include:

Least expensive MSDS programs

You’ll find relatively inexpensive Master of Data Science programs at:


Least expensive online MSDS programs

Affordable online Master of Data Science programs include:

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About the Author

Tom Meltzer began his career in education publishing at The Princeton Review, where he authored more than a dozen titles (including the company's annual best colleges guide and two AP test prep manuals) and produced the musical podcast The Princeton Review Vocab Minute. A graduate of Columbia University (English major), Tom lives in Chapel Hill, NC.

About the Editor

Tom Meltzer spent over 20 years writing and teaching for The Princeton Review, where he was lead author of the company's popular guide to colleges, before joining Noodle.

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