There are more than 30 great universities that offer an online master’s in data science program. Some of these programs offer specializations, such as analytics, artificial intelligence or data engineering. Part-time and full-time enrollment options are available for online data science degrees.
Experience industry-focused education, with our sponsored programs, that could boost your career and make an impact.
UNC-Chapel Hill’s online Master of Applied Data Science program gives you a holistic understanding of the data life cycle, preparing you to effectively — and ethically — collect, process, manage and analyze data. Learn to translate your insights into a clear narrative that can be used to drive action. Math and programming skills required.
In today’s data-driven world, there’s an increased need for data scientists in every industry. Accelerate your career in this rapidly growing field by completing a rigorous master’s degree program that’s 100% online, affordable, and flexible to fit your life. With an MSDS from the top-ranked University of Texas at Austin, you’ll gain in-demand skills in data visualization, data mining, data analysis, machine learning, and more.
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In a world of online learning, university name recognition could still be valuable for students and potential future employers. Before considering a data science degree from a well-known university, here are some questions to ask to ensure you are enrolling in a valuable data science program.
As you consider enrolling in a data science program, you might wonder about the longevity and experience of the degree you’re pursuing. While the exact duration for which a data science degree has been offered may vary between institutions, numerous online programs have emerged.
Certain institutions offer real-time, face-to-face interactions with peers, while others provide asynchronous learning opportunities through discussion posts. Numerous online master’s programs in data science may utilize one or both of these methods, but individual preferences ultimately play a significant role in determining the best fit.
While pursuing degrees, data science students have access to a variety of resources and datasets through reputable online programs. Another option is seeking opportunities for coding competitions through platforms like Kaggle. Programs may also feature research centers, entrepreneur initiatives, private and public partnerships, laboratories, incubators, and sponsored competitions. Such opportunities allow you to gain hands-on experience and practical knowledge, enhancing your learning experience throughout your degree.
Trying to decide which online master’s in data science is best for you? As a general gauge of quality, you might look at university rankings in quantitative analysis, computer science, and statistics as well as review any awards for online offerings.
| University | Study Length | Test Requirement | Coursework | Project-Based Learning |
|---|---|---|---|---|
| Syracuse University SPONSORED | Complete in as few as 18 months | No GRE Required | – Data Analytics – Machine Learning – Natural Language Processing | Project-Based Curriculum |
| The University of North Carolina at Chapel Hill SPONSORED | As few as 16 months | No GRE Required | N/A | N/A |
| University of California, Berkeley SPONSORED | As few as 12 months | No GRE Required | – Deep Learning in the Cloud and at the Edge – Machine Learning – Statistics for Data Science | Project-Based Coursework |
| University of Denver SPONSORED | As few as 18 months | No GRE Required | – Advanced Probability and Statistics for Data Science – Machine Learning – Python Software Development | Project-Based Curriculum |
| Southern Methodist University SPONSORED | Complete in as few as 20 months | No GRE Required | – Cloud Computing – Machine Learning – Statistical Foundations for Data Science | Some Project-Based Learning |
| University of Texas at Austin SPONSORED | 1.5 – 3 Years | No GRE Required | N/A | N/A |
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For more information on the factors listed in this table, view a list of descriptions for each factor.
| University | Study Length | Test Requirement | Coursework | Project-Based Learning |
|---|---|---|---|---|
| Bay Path University | 1+ years | No GMAT/GRE Required | – Deep Learning – Machine Learning – Statistical Modeling | No Project-Based Learning |
| Bellevue University | 1+ years | No GRE Required | – Predictive Analytics – Statistics for Data Science | Some Project-Based Learning |
| Cabrini University | 2 years | No GRE Required | – Information Systems Management – Machine Learning – Natural Language Processing | No Project-Based Learning |
| Central Connecticut State University | N/A | N/A | – Algorithms for Data Science – Database Systems | No Project-Based Learning |
| City University of New York (CUNY) | 1+ years | No GRE Required | – Advanced Programming Techniques – Business Analytics – Machine Learning | Project-Based Curriculum |
| Clarkson University | 1+ years | No GRE Required | N/A | N/A |
| DePaul University | N/A | N/A | N/A | N/A |
| Drexel University | 2+ years | No GRE Required | – Artificial Intelligence – Machine Learning – Responsible Data Analysis | No Project-Based Learning |
| Elmhurst University | 2 years | No GRE Required | – Business Intelligence – Data Mining – Data Warehousing – Machine Learning | Some Project-Based Learning |
| Harvard University | 1.3+ years | GMAT/GRE Required | N/A | N/A |
| Indiana University | 1+ years | No GRE Required | N/A | N/A |
| Johns Hopkins University | 1+ years | No GRE Required | N/A | N/A |
| Lewis University | 18 – 24 months | No GMAT/GRE Required | – Artificial Intelligence – Data Mining and Analytics – Machine Learning | No Project-Based Learning |
| Merrimack College | 1+ years | No GRE Required | – Data Governance – Machine Learning – Predictive Modeling | No Project-Based Learning |
| New England College | 2 years | GRE Required | N/A | N/A |
| Northcentral University | 1.5+ years | No GRE Required | – Big Data Integration – Data Mining | No Project-Based Learning |
| Oklahoma State University | 2 years | GMAT/GRE Required | N/A | N/A |
| Regis University | 1+ years | N/A | N/A | N/A |
| Rochester Institute of Technology | 2 years | No GRE Required | – Linear Models – Regression – Software Engineering For Data Science and Applied | No Project-Based Learning |
| Southern Methodist University SPONSORED | Complete in as few as 20 months | No GRE Required | – Cloud Computing – Machine Learning – Statistical Foundations for Data Science | Some Project-Based Learning |
| Saint Mary’s College | 2 years | No GRE Required | – Data Mining at Scale – Database Systems | Project-Based Curriculum |
| Syracuse University SPONSORED | Complete in as few as 18 months | No GRE Required | – Data Analytics – Machine Learning – Natural Language Processing | Project-Based Curriculum |
| Technological University Dublin | 2 years | No GRE Required | N/A | N/A |
| University of California, Berkeley SPONSORED | As few as 12 months | No GRE Required | – Deep Learning in the Cloud and at the Edge – Machine Learning – Statistics for Data Science | Project-Based Coursework |
| University of California – Santa Barbara | N/A | No GRE Required | N/A | N/A |
| University of California Los Angeles | 24 Months | GRE Required | – Big Data Analytics – Digital Speech Processing – Machine Learning | No Project-Based Learning |
| University of Denver SPONSORED | As few as 18 months | No GRE Required | – Advanced Probability and Statistics for Data Science – Machine Learning – Python Software Development | Project-Based Curriculum |
| University of Illinois at Urbana-Champaign | 1+ years | No GMAT/GRE Required | N/A | N/A |
| The University of Kansas | 1+ years | No GRE Required | N/A | N/A |
| University of Missouri | 2 years | No GRE Required | – Cloud Computing for Data Analytics – Machine Learning – Parallel Computing for Data Science | Project-Based Curriculum |
| The University of North Carolina at Chapel Hill SPONSORED | As few as 16 months | No GRE Required | N/A | N/A |
| University of Notre Dame | 2+ years | No GRE Required | – Ethics And Policy in Data Science – Machine Learning – Time Series and Forecasting | Some Project-Based Learning |
| University of the Pacific | 2 years | No GMAT/GRE Required | N/A | N/A |
| University of Texas at Austin SPONSORED | 1.5 – 3 Years | No GRE Required | N/A | N/A |
| University of Virginia | Less than 1 year | No GRE Required | N/A | N/A |
| University of Wisconsin | 1+ years | No GRE Required | N/A | N/A |
| Wayne State University | 1+ years | GMAT Required | N/A | N/A |
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Name of Degree: Master of Science in Data Science
Enrollment Type: Full-Time and Part-Time
Length of Program: 1+ years
Credits: 36
Concentrations: N/AAdmission Requirements:
Name of Degree: Master of Science in Data Science
Enrollment Type: Full-Time and Part-Time
Length of Program: 1+ years
Credits: 30
Concentrations: N/AAdmission Requirements:
Name of Degree: Master of Science in Data Science
Enrollment Type: Part-Time
Length of Program: 2 years
Credits: 36
Concentrations: N/AAdmission Requirements:
Name of Degree: Master of Science in Data Science
Enrollment Type: N/A
Length of Program: N/A
Credits: 31
Concentrations:Bioinformatics, Text Analytics, Computational Methods, Advanced Methods in Data Science
Admission Requirements:
Name of Degree: Master of Science in Data Science
Enrollment Type: Full-Time and Part-Time
Length of Program: 1+ years
Credits: 30
Concentrations: N/AAdmission Requirements:
Name of Degree: Master of Science in Applied Data Science
Enrollment Type: Full-Time and Part-Time
Length of Program: 1+ years
Credits: 36
Concentrations: N/AAdmission Requirements:
Name of Degree: Master of Science in Data Science
Enrollment Type: N/A
Length of Program: N/A
Credits: 52
Concentrations:Computational Methods, Health Care, Marketing, Hospitality
Admission Requirements:
Name of Degree: Master of Science in Data Science
Enrollment Type: Full-Time and Part-Time
Length of Program: 2+ years
Credits: 45
Concentrations: N/AAdmission Requirements:
Name of Degree: Master of Science in Data Science and Analytics
Enrollment Type: Full-Time
Length of Program: 2 years
Credits: 30
Concentrations: N/AAdmission Requirements:
Name of Degree: “Master of Science in Health Data Science “
Enrollment Type: Full-Time and Part-Time
Length of Program: 1.3+ years
Credits: 60
Concentrations: N/AAdmission Requirements:
Name of Degree: Master of Science in Data Science
Enrollment Type: Full-Time and Part-Time
Length of Program: 1+ years
Credits: 30
Concentrations:Data analytics and visualization, Intelligent systems engineering, Cybersecurity
Admission Requirements:
Name of Degree: Master of Science in Data Science
Enrollment Type: Part-Time
Length of Program: 1+ years
Credits: 30
Concentrations: N/AAdmission Requirements:
Name of Degree: Master of Science in Data Science
Enrollment Type: Full-Time and Part-Time
Length of Program: 18 – 24 months
Credits: 33
Concentrations:Cognitive and Prescriptive Analytics, Data Engineering, Cybersecurity Data Science, Data Science Research, Computational Biology and Bioinformatics
Admission Requirements:
Name of Degree: Master of Science in Data Science
Enrollment Type: Full-Time and Part-Time
Length of Program: 1+ years
Credits: 32
Concentrations: N/AAdmission Requirements:
Name of Degree: Master of Science in Data Science
Enrollment Type: Part-Time
Length of Program: 2 years
Credits: 36
Concentrations: N/AAdmission Requirements:
Name of Degree: Master of Science in Data Science
Enrollment Type: N/A
Length of Program: 1.5+ years
Credits: 30
Concentrations: N/AAdmission Requirements:
Name of Degree: Master of Science in Business Analytics and Data Science
Enrollment Type: Full-Time and Part-Time
Length of Program: 2 years
Credits: 33
Concentrations: N/AAdmission Requirements:
Name of Degree: Master of Science in Data Science
Enrollment Type: Full-Time and Part-Time
Length of Program: 1+ years
Credits: 36
Concentrations: N/AAdmission Requirements:
Name of Degree: Master of Science in Data Science
Enrollment Type: Full-Time
Length of Program: 2 years
Credits: 30
Concentrations: N/AAdmission Requirements:
DataScience@SMU is an online Master of Science in Data Science program that prepares students with the fundamental skills needed to be sought-after data science leaders in a variety of industries. Our students learn from cross-disciplinary SMU faculty in online classrooms with a small student-to-professor ratio. DataScience@SMU students gain highly in-demand skills in working with unstructured data, big data processing, statistical analysis, text mining, machine learning, and business analytics.
Enrollment Type: Full-Time and Part-Time
Length of Program: Complete in as few as 20 months
Credits: 33.5
Name of Degree: Master of Science in Data Science
Enrollment Type: Full-Time
Length of Program: 2 years
Credits: 36
Concentrations: N/AAdmission Requirements:
The online Master of Science in Applied Data Science at Syracuse University is an interdisciplinary program offered by the School of Information Studies and the Martin J. Whitman School of Management. The curriculum requires 36 credits, including electives that allow candidates to develop deep knowledge in their area of specialty. Students must compile a portfolio of projects to submit for faculty review. Online course content focuses on collaboration and includes face-to-face classes, project-based work, and 24/7 access to materials. Applicants must have a bachelor’s degree and must submit transcripts, a personal statement, resume, and two recommendations. No GRE Scores Required.
Enrollment Type: Full-Time and Part-Time
Length of Program: Complete in as few as 18 months
Credits: 34
Name of Degree: Master of Science in Computing in Applied Data Science & Analytics
Enrollment Type: Part-Time
Length of Program: 2 years
Credits: 7
Concentrations: N/AAdmission Requirements:
Designed for data science professionals, the UC Berkeley School of Information’s Master of Information and Data Science (MIDS) prepares students to derive insights from real-world data sets, using the latest tools and analytical methods to interpret and communicate their findings in ways that influence decision-making. The MIDS program features a rigorous, multidisciplinary curriculum focused on problem solving, preparing students to creatively apply methods of data collection, analysis, and presentation to address and solve problems in areas of human interest, government, and business.
Enrollment Type: Full-Time and Part-Time
Length of Program: As few as 12 months
Credits: 27
Name of Degree: Master of Arts in Statistics – Data Science Track
Enrollment Type: Full-Time and Part-Time
Length of Program: N/A
Credits: 42
Concentrations: N/AAdmission Requirements:
Name of Degree: Master of Science in Engineering with Certificate of Specialization in Data Science Engineering
Enrollment Type: N/A
Length of Program: 24 Months
Credits: 36
Concentrations: N/AAdmission Requirements:
The online Master of Science in Data Science from the University of Denver Ritchie School prepares students to build the tools that collect data and use evaluation strategies to make critical business, research, and policy decisions. The program is designed for students of all academic backgrounds and offers three bridge courses to provide them with the programming skills they need to start classes. A bachelor’s degree is required to apply.
Enrollment Type: Full-Time and Part-Time
Length of Program: As few as 18 months
Credits: 48
Name of Degree: Master of Computer Science in Data Science
Enrollment Type: Full-Time and Part-Time
Length of Program: 1+ years
Credits: 32
Concentrations: N/AAdmission Requirements:
Name of Degree: Applied Statistics, Analytics & Data Science Graduate Program
Enrollment Type: Full-Time and Part-Time
Length of Program: 1+ years
Credits: 30
Concentrations: N/AAdmission Requirements:
Name of Degree: Master of Science in Data Science and Analytics
Enrollment Type: Full-Time and Part-Time
Length of Program: 2 years
Credits: 34
Concentrations:Geospatial, BioHealth Analytics, High Performance Computing, Human-Centered Science Design, Data Journalism/Strategic Communication
Admission Requirements:
UNC-Chapel Hill’s online Master of Applied Data Science program gives you a holistic understanding of the data life cycle, preparing you to effectively — and ethically — collect, process, manage and analyze data. Learn to translate your insights into a clear narrative that can be used to drive action. Math and programming skills required.
Enrollment Type: Full-Time and Part-Time
Length of Program: As few as 16 months
Credits: 30
Name of Degree: Master of Science in Data Science
Enrollment Type: Full-Time
Length of Program: 2+ years
Credits: 30
Concentrations: N/AAdmission Requirements:
Name of Degree: Master of Science in Data Science
Enrollment Type: Full-Time
Length of Program: 2 years
Credits: 32
Concentrations: N/AAdmission Requirements:
In today’s data-driven world, there’s an increased need for data scientists in every industry. Accelerate your career in this rapidly growing field by completing a rigorous master’s degree program that’s 100% online, affordable, and flexible to fit your life. With an MSDS from the top-ranked University of Texas at Austin, you’ll gain in-demand skills in data visualization, data mining, data analysis, machine learning, and more.
Enrollment Type: Full-Time and Part-Time
Length of Program: 1.5 – 3 Years
Credits: 30
Admission Requirements:
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Name of Degree: Master of Science in Data Science
Enrollment Type: Full-Time and Part-Time
Length of Program: Less than 1 year
Credits: 32
Concentrations: N/AAdmission Requirements:
Name of Degree: Master of Science in Data Science
Enrollment Type: Full-Time and Part-Time
Length of Program: 1+ years
Credits: 36
Concentrations: N/AAdmission Requirements:
Name of Degree: Master of Science in Data Science and Business Analytics
Enrollment Type: Full-Time and Part-Time
Length of Program: 1+ years
Credits: 30
Concentrations: N/AAdmission Requirements:
An online master’s in data science bears the same weight as a campus degree on your résumé. An online degree may offer more flexibility and better work-life balance. It may also reduce the total cost by eliminating a commute to campus.
Admissions criteria for an online data science master’s and certificates vary, so make sure the program fits your skill level.
Programs can include a blend of core courses and electives. Common elements include multimedia lectures, discussion boards, video conferencing, group project work, and virtual office hours.
Occasionally, some data science online degree programs expect you to come to campus at some point (or points) during your study. Sometimes it’s a handful of weekends, and other times it’s a longer, more concentrated session. Certain schools will have a kick-off and a final reunion week. Be sure to confirm what’s required.
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To get the most value from a data science degree and for students to become data scientists, coursework should be just as demanding as traditional coursework. Typical courses may include database systems, natural language processing, and data analysis.
In addition, some master’s programs will have:
Compare the data science program’s curriculum with the university’s campus offering. If it’s radically different, ask yourself if this is your best option.
Data science and computer science are two distinct disciplines within the realm of technology and computing, both playing roles in today’s data-driven world. Data scientists use mathematics, statistics, and computer science to extract insights from structured and unstructured data. The primary goal for data science professionals is to leverage techniques such as data mining, machine learning, and data visualization to identify patterns, make predictions, and drive decision-making.
In contrast, computer science is a broader discipline that encompasses the study of algorithms, programming languages, software development, computer hardware, and computational theory. Computer science graduates pursue roles as software developers, network administrators, computer systems analysts, and cybersecurity specialists, among others, contributing to the development and maintenance of software and hardware systems that underpin modern society.
Admissions requirements vary depending on the university, but some common requirements may include:
There are quite a few benefits of learning data science online, but one of the benefits of earning a data science degree online is flexibility. Most universities have part-time options for students who work full-time and do not require on-campus visits for budding data scientists. Asynchronous programs are completely self-paced, so students are in full control of their schedules and can manage their work-life balance.
Other programs offer synchronous teaching, which is more hands-on and interactive with students viewing lectures at a scheduled time. Some universities offer online master’s in data science programs year-round, so there’s no need to wait for specific dates to apply and get started.
Many data science programs have faculty who have worked as data scientists and teach students the practical skills they need through an experiential learning process. Read up on the data science faculty profiles — these folks should be some of the same people teaching campus programs.
If they’re full-time tenured professors, are they working on cutting-edge research or collaborating with campus partners (e.g., IBM)? Have they won data science awards or research fellowships? It’s valuable to have individuals who are knowledgeable about events and developments outside of academic institutions. If they’re part-time, are they leading data projects in real companies? Do they have experience with start-ups and incubators? Again, you may want data scientists as professors to help you understand today’s landscape.
Along with the brand name (e.g., MIT), networking with data scientists is part of the reason students opt for university programs over alternatives. That casual chat in the hall or a coffee with your data science program professor may just lead to an AI project or a government contract.
It can be harder to build relationships while earning an online data science master’s, but it’s not impossible. Try looking for programs that incorporate networking opportunities, corporate days, guest speakers, and/or on-campus visits. Think about whether a program in a tech hub (e.g., the Bay Area) or a foreign city (e.g., London) might help you forge new business contacts.
You might also want to examine the class profile to see whom you’ll be meeting.
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An online data science master’s offers the same comprehensive curriculum as a traditional data science program but is delivered digitally. This enables students to access course materials, engage with instructors and peers, and complete assignments from anywhere with an internet connection, providing flexibility for those with full-time jobs or other commitments. Either modality can help students in becoming data scientists.
While both data science and computer science involve programming and data analysis, data scientists focus more on extracting insights from large datasets using statistical methods, machine learning, and data visualization techniques. In contrast, computer science primarily emphasizes software development, algorithms, and computer systems.
Yes, many online data science master’s offer elective courses or concentration areas in specialized fields such as natural language processing, allowing budding data scientists to tailor their learning experience to their interests and career goals. Natural language processing is a subfield of artificial intelligence that focuses on the interaction between computers and humans through the understanding, interpretation, and generation of human language.
When you graduate from a data science program, you can pursue various roles as data scientists, data analysts, machine learning engineers, and more across a wide range of industries, including technology, finance, healthcare, and government. Their expertise in database systems, data analysis, and machine learning methods enables them to solve complex problems and drive data-driven decision-making.
Yes, most data science degrees provide students access to real-world datasets and hands-on projects, enabling them to apply their newly acquired skills and knowledge to practical scenarios. This experience is invaluable in preparing students for success as data science professionals in their future careers.
The primary difference between data scientists and data analysts lies in the scope of their work, the complexity of the problems they tackle, and the techniques they use. While both roles involve working with data and extracting insights, data scientists often deal with more advanced analytics, predictive modeling, and machine learning, whereas data analysts focus on descriptive analytics and reporting. Data scientists delve deeper into data analysis by creating predictive models, employing machine learning algorithms, and working with unstructured or large-scale data.
UNC-Chapel Hill’s online Master of Applied Data Science program gives you a holistic understanding of the data life cycle, preparing you to effectively — and ethically — collect, process, manage and analyze data. Learn to translate your insights into a clear narrative that can be used to drive action. Math and programming skills required.
In today’s data-driven world, there’s an increased need for data scientists in every industry. Accelerate your career in this rapidly growing field by completing a rigorous master’s degree program that’s 100% online, affordable, and flexible to fit your life. With an MSDS from the top-ranked University of Texas at Austin, you’ll gain in-demand skills in data visualization, data mining, data analysis, machine learning, and more.
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Last updated April 2023.
