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Top Ranked Data Science Programs: Should You Pay Attention?

It hasn’t been a good couple years for college rankings. In mid-November 2022, years of tension boiled over: Yale and Harvard Law Schools announced they were pulling out of the US News & World Report’s famous and industry-standard rankings. These schools argued, in part, that the rankings unfairly reward schools for scholarship-granting processes that juice LSAT scores and punish them when their graduates take lower-paying public sector jobs. Since then, dozens of colleges and universities have followed suit, ending their cooperation with US News & World Report and refusing to share data, in turn prompting US News CEO Eric Gertler to suggest in Wall Street Journal op-ed that the elite law and medical schools that began the exodus simply don’t like being held accountable. As it stands, it seems likely the defections will continue.

The crisis in confidence in ranking outfits like US News has serious implications for those researching future-defining educational degree programs, especially since these programs, in the US at least, are so gosh-darned expensive. Enrolling in one of these programs is a big financial decision, and would-be undergraduate and graduate students are increasingly skeptical of the returns, as The Economist explains in a recent piece titled “Was your degree really worth it?” These rankings thus ostensibly serve as crucial data as students are forecasting potential return on investment for their future degrees.

Will your data science degree be worth it?

The situation for the aspiring data scientist is nuanced by the fact that data science is a booming career — more than data analyst and even machine learning engineer — with the US Bureau of Labor Statistics projecting 36% growth in headcount by 2031 and $100,910 median pay, respectively. Compare this to just 5% growth and $45,760 median pay across the US labor market as a whole, and it’s clear just how sunny this outlook is.

But given how relatively new data science is as a field, let alone an academic subject, and how likely it is that you’ll need to ultimately earn a master’s degree to succeed in data science (a recent report suggests that over two-thirds of data science professionals hold such a credential), it makes sense that someone considering a data science career would consult one or several of the many websites out there professing to possess the list of top ranked data science programs. And this is even considering that if you have high aptitude and pursue a data science master’s degree or other data science course of study, there’s a very good chance you’ll see substantial return on investment.

The state of data science master’s program rankings

Of course, the data science master’s program rankings aren’t immune from systemic issues like faulty methodology, and they suffer also from the fact that many of the rankers are frequently in commercial partnerships with one or more of the providers. While some ranking outfits use US News rankings as an input for their own rankings (“garbage in, garbage out,” as they say) or employ shoddy methodologies (without naming names), others feature their partner schools prominently above ranked schools on webpages in a cynical attempt to win monetizable click-throughs. This cynicism can’t but cast doubt on the accuracy of the rankings, at least in our minds.

What role should rankings play in your decision?

This is where we introduce our own brilliant methodology and make the claim that our rankings — and only ours — are the true rankings, right? Wrong. While we will present our favorite in-person and online data science master’s programs at the end of this article (we are at present not compensated by any school or university for doing so), we don’t want you to simply take our word for it. We also don’t want you to blindly believe what other ranking outfits have to say. But that doesn’t mean that the programs we present below and the rankings you can find elsewhere on the internet aren’t valuable. 

These days, if you’re looking for a data science education you’re spoiled for choice. There are loads of great data science certificates, bootcamps, and bachelor’s programs to choose from, and the same is true for master’s programs. You need some way to narrow down the field, and online lists, compendia, and rankings can be a great way to do so.

But never take one person’s or website’s option as an absolute, and make sure you’re doing your own due-diligence, and, better yet, guerilla research.

Your data science career starts before your master’s degree

You’re looking into data science master’s programs because you want to be a data scientist (or want to be a better data scientist). So start now! Take in the data you can find, ruthlessly question its veracity, and determine the role it should play in your decision-making process. And go guerilla: make LinkedIn work for you by finding graduates of the programs you’re interested in and seeing for yourself if the kinds of outcomes you’re seeing justify the costs of the program. Better yet, reach out to them, introduce yourself, and see if they’ll connect for a short conversation so you can ask them about their experience with the program. This way, you can connect real faces to the program and be sure of the quality of the data instead of blindly trusting some opaque ranking body.

In addition to leveraging LinkedIn, get up close and personal with the programs themselves. If a program featured on a best-of list interests you, head to the website and see how they characterize themselves. Check out their faculty members and, even better, look into the research projects these faculty members have been a part of. Does their work excite you? Can you see them leading you where you want to go? Do you have questions for them? If so, reach out. Most faculty will have their institutional emails listed on their faculty pages, and the worst thing they can do is ghost you. If they respond, you might not only get a response to your question, you might get that much closer to a favorable admissions decision.

Our smooth transition: what do we look for in a data science program?

Now that you know to trust but verify when it comes to rankings, best-of lists, and the rest, we’ll get down to explaining our thinking as we compiled the picks for in-person and online data science master’s programs featured below.

In general, we want a data science master’s program to teach the following skills:

  • Computer science and information technology: Excel, SQL, coding with Python or R programming language

  • Applied mathematics: statistics, probability, linear algebra

  • Machine learning: supervised learning, unsupervised learning, reinforcement learning, deep learning, and relevant software libraries like Pandas

  • Data skills: data engineering, data mining, data visualization, data management along the entire data pipeline

  • Data science principles and core skills:research design, data science ethics, data storytelling

  • We also want a data science master’s program to provide ample opportunity for the practical application of the skills learned, including through a final faculty-supervised capstone project.

  • In addition to this basic curriculum, we weigh the following in determining whether to count a program among our favorites:

  • Reputation: Will a school’s name get your resume noticed? Does it come with an active alumni community?

  • Career-Readiness: Is the program interested not just in producing good students, but in producing good data scientists? Is there outcome data that affirms this?

  • Modality: Does a school offer its curriculum online, in-person, or both? Are there options for part-time, full-time, and self-paced study?

  • Cost: Given all of these other factors, does a school offer good value for money? Can a student with a good work ethic and an aptitude for data science reasonably expect to see a good return on investment in the near- or mid-term?

With all that in mind, our picks also seek to highlight courses that provide feasible paths to a data science career for individuals from all backgrounds and for all price-points. So what you see below isn’t a ranking, but rather a set of programs to kick off your research and give you a better idea of what’s out there.

Our picks for in-person data science master’s programs

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University of Washington

Seattle, WA

The University of Washington's MS in Data Science (MSDS) program trains graduate students in statistical modeling, machine learning, data management, software engineering, research design, and more. A year-long capstone profit allows students to apply what they’ve learned in non-profit and for-profit industries.

Alumni have gone on to work at Boeing, Amazon, Google, Facebook, Microsoft, Zillow, T-Mobile, and the Institute for Health Metrics and Evaluation. The average alumnus or alumna earns a salary of $124,000.

UW’s data science program is committed to diversity with an incoming cohort that is 58% women and 59% international students.

Selected Courses:

  • Software Design for Data Science

  • Applied Statistics & Experimental Design

  • Statistical Machine Learning for Data Science

Program Length & Modality:

  • Full Time: 1.5 years, 2 course per quarter, 40 hours per week in Seattle

  • Part Time: 2.5 years, 1 course per quarter, 20 hours per week in Seattle

Prerequisites & Requirements

  • Minimum equivalent of four-year bachelor’s degree from regionally accredited US college or university or foreign equivalent

  • Official transcripts

  • Minimum 3.0 GPA, with possibility of admission petition for lower GPA

  • International students who are non-native English speakers must demonstrate English proficiency through minimum scores on TOEFL (80), Duolingo (105), or IELTS (6.5).

  • Minimum skills in mathematics, computer science, and computer programming (equivalent of Calculus III, Linear Algebra, and two introductory-level programming courses)

Tuition: ~$17,000/year (resident) / ~$32,000/year (non-resident)

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Northwestern University

Evanston, Illinois

Northwestern’s Master’s in Data Science program focuses on teaching students how to code with languages like R and Python in environments like Go, TensorFLow, and Keras. Students complete 12 courses in total: six core courses, two specialization courses, two electives, and a capstone project. Online students have six options for their specialization: Analytics and Modeling, Analytics Management, Artificial Intelligence, Data Engineering, General Data Science, or Technology Entrepreneurship.

Selected Courses:

  • Database Systems and Data Preparation

  • Real-Time Stream Processing and Analytics

  • Data Governance, Ethics, and Law

Program Length & Modality: Between 2 and 5 years, live online or one-year hybrid (2 on-campus and 1 online course per semester)

Prerequisites & Requirements

  • Aptitude or existing skill in programming and applied mathematics

Tuition: $58,860

university of michigan seal

University of Michigan

Ann Arbor, Michigan

UMich’s Data Science Master’s Program is a joint degree offered by the departments of Computer Science and Engineering, Department of Statistics, School of Information, and Department of Biostatistics. Students graduate able to leverage statistical and computational techniques to answer questions in a variety of corporate and governmental settings as well as design and implement their own techniques.

Selected Courses:

  • Database Management Systems

  • Computational Data Science and Machine Learning

  • Advanced Data Mining

Program Length & Modality: 2 years, on campus

Prerequisites & Requirements:

  • Undergraduate degree

  • 2 semesters of college calculus

  • 1 semester of linear algebra

  • 1 intro to computing course

Tuition: $25,894 (in-state); $52,124 (out-of-state)

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Columbia University

New York, NY

Columbia’s MS in Data Science offers students the chance to undertake their own research, complete a capstone project, and learn from Columbia’s award-winning faculty.

Graduates from Columbia’s masters degree program work at Goldman Sachs, Amazon, Google, J.P. Morgan, McKinsey, LinkedIn, NBC Universal, Starbucks, and Walmart.

Selected Courses:

  • Exploratory Data Analysis and Visualization

  • Computer Systems for Data Science

  • Big Data in Finance

  • Natural Language Processing: Computational Models of Social Meaning

Program Length & Modality: Generally 2 years, on campus

Total Credits: 30

Prerequisites & Requirements

  • Undergraduate degree

  • Prior quantitative and programming coursework

  • TOEFL, IELTS, or PTE Academic test scores for international applicants who aren’t English native-speakers

Tuition: ~$71,000 for entire program

Our picks for online data science master’s programs

johns hopkins university seal

Johns Hopkins University

Johns Hopkins’ Data Science Master’s Program Online helps working professionals build skills in areas like data visualization, machine learning, data systems, and game theory. Students take seven required courses, two applied and computational mathematics electives, and one computer science elective. Those with existing skills can place out of certain requirements through a proficiency exam. In addition to the master’s program, Johns Hopkins offers a graduate certificate and a post-master’s certificate in data science.

Selected Courses

  • Data Engineering Principles and Practice

  • Big Data Processing Using Hadoop

  • Data Mining

Program Length & Modality: Part-time within 5 years, live online

Prerequisites & Requirements:

  • Three semesters of calculus

  • One semester of advanced mathematics

  • One semester of Python or Java

Tuition: Approximately $49,200


The University of Texas at Austin

UT Austin’s Master of Science in Data Science online program offers a flexible and inexpensive way to gain graduate-level training in simulation, visualization, machine learning, and optimization. Delivered through the edX platform, the materials for this online master’s program are created and supervised by UT Austin faculty and staff.

Program Highlights:

  • Probability and Simulation-Based Inference for Data Science

  • Foundations of Regression and Predictive Modeling

  • Reinforcement Learning

Program Length & Modality: Between 2 and 6 years, self-paced online 

Prerequisites & Requirements:

  • Bachelor’s degree from accredited institution

  • 3.0 GPA or higher preferred

  • Must demonstrate knowledge of mathematics and programming through coursework, experience and MSDS Quest Assessment

Tuition: $10,000

drexel seal

Drexel University

Drexel’s Online Masters Degree in Data Science gives students the option of three possible elective tracks — Analytics, Mining, and Algorithms; Visualization and Communications; Management and Accountability — to complement core data science courses focusing on cloud computing, machine learning, data acquisition, and more. Students also complete a capstone project. 

Those holding bachelor’s degrees who don’t wish to complete a full data science degree program or who aren’t yet qualified can instead take one of Drexel’s certificate programs in applied data science, computational data science, or big data analytics.

Program Highlights:

  • Applied Machine Learning for Data Science

  • Introduction to Data Analytics

  • Healthcare Informatics

  • Disaster Recovery, Continuity Planning, and Digital Risk Assessment

Program Length & Modality: As little as 1 year, online

Prerequisites & Requirements:

  • Four-year bachelor’s degree. If not in Computer Science, Software Engineering, or Math, additional prerequisites may be required.

  • 3.0 GPA preferred

Tuition: $62,820

Georgia Tech logo mark

Georgia Institute of Technology

Georgia Tech’s Online Master of Science in Analytics is an affordable data science master’s degree from one of the nation’s top-ranked data science and data analytics programs. Students are able to take courses in big data analytics, visual analytics, computing, and statistics at their own pace. They also take elective courses online with the same professors who offer classes on campus in one of three areas of specialization: Analytical Tools, Business Analytics, or Computational Data Analytics. The program culminates with a six-hour practicum in which students apply what they’ve learned in an independent project.

Program Highlights:

  • Introduction for Computing for Data Analytics

  • Computational Data Analysis

  • Applied Natural Language Processing

Program Length & Modality: 24–72 months, self-paced online

Prerequisites & Requirements:

  • At least one course in probability, programming, or calculus and linear algebra

  • GRE and GMAT optional

Tuition: $9,900

What’s next?

If you see a program that interests you, don’t hesitate to check it out and then dive in for some guerrilla research. But again, don’t just take our word for it: there are many publications out there purporting to have identified the best data science programs in master’s degrees offered by UC Berkeley, Stanford University, Harvard University, or many other schools. While they may or may not be right, they can point you to programs that you might find fit your background, needs, and interests. If you’re looking for more of our picks for data science educational paths, you can also find them here: