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Connect With UsMS in Data Science Program Overview
The Tufts Master of Science in Data Science program will help you acquire the skills and qualifications necessary to master data, interpret its significance, and effectively communicate actionable insights. Whether it’s in the context of climate, supply chain, or healthcare, the Tufts data science master’s program will provide you with the analytical expertise needed to facilitate high-level, data-driven decision-making.
Data Science Career Outlook
In an increasingly data-centric world, data science job opportunities are expected to grow 35 percent by 2032,* and the average salary of U.S. data science master’s degree holders exceeds $100,000.**
The Tufts Master of Science in Data Science program emphasizes innovation and discovery, preparing you to pursue better job opportunities and research projects, advance within your current organization, and increase your earning potential. Students learn the data analysis techniques they need to enter a market vying for new and ambitious talent and to be prepared to set the standard for future data science careers.
*U.S. Bureau of Labor Statistics, 2024
**Payscale, September 2024
Explore high-demand jobs you can pursue with an online data science master’s degree and the average salary you could earn:
| Job title | Average salary |
|---|---|
| Data science manager | $146,695 |
| Database architect | $125,525 |
| Machine learning engineer | $119,197 |
| Analytics manager | $106,188 |
| Data scientist | $100,942 |
| Data engineer | $97,330 |
| Statistician | $90,144 |
| Database administrator | $79,454 |
Source: Payscale, September 2024.
Salaries shown above are based on average salaries reported to Payscale as of September 2024.
Why Choose Tufts University for Your Master’s in Data Science Online?
A world-class research institution, Tufts develops leaders who advocate for public good. As an MS in Data Science online student in the School of Engineering, you’ll join a collaborative community and gain a forward-thinking, innovative mindset through hands-on experiences and industry partnerships.
Interested in data science but don’t have prior experience?
Explore the Pathway to MS in Data Science ProgramMS in Data Science Courses and Curriculum
In today’s data-driven world, organizations rely on data to make informed decisions and solve problems efficiently. The Tufts online MS in Data Science (MSDS) program equips you with the skills to excel in data-centric problem-solving across various industries. You will master data mining, machine learning, Python, and systems analysis, becoming an efficient problem solver through hands-on data interpretation and communication. This interdisciplinary MS in Data Science program prepares you to lead at the intersection of data and decision-making. Upon completion, you’ll have the skills to formulate complex problems, collect and interpret data, apply analysis techniques, and evaluate solutions effectively.
Core Requirements
4 Credits
“Big data” deals with techniques for collecting, processing, analyzing, and acting on data at internet scale: unprecedented speed, scale, and complexity. This course introduces the latest techniques and infrastructures developed for big data, including parallel and distributed database systems, map-reduce infrastructures, scalable platforms for complex data types, stream processing systems, and cloud-based computing. You’ll learn to apply common statistical and machine learning techniques to large data sets. Course content will be a blend of theory, algorithms, and practical, hands-on work.
3 Credits
This course provides an overview of methods by which computers can learn from data or experience and make decisions accordingly. Topics include supervised learning, unsupervised learning, reinforcement learning, and knowledge extraction from large databases with applications to science, engineering, and medicine. You’ll learn to recognize a problem as being appropriate for a machine learning solution and take steps to solve that problem with an applicable technique.
3 Credits
Advanced analysis in probabilistic systems with strong emphasis on theoretical methods. Development of analytical tools for the modeling and analysis of random phenomena with application to problems across a range of engineering and applied science disciplines. Probability theory, sample and event spaces, discrete and continuous random variables, conditional probability, expectations and conditional expectations, and derived distributions. Sums of random variables, moment generating functions, central limit theorem, laws of large numbers. Statistical analysis methods, including hypothesis testing, confidence intervals, and nonparametric methods. Undergraduates may not take both EE 0024 and EE 0104 for degree credit. Prerequisite: Math 0042 or equivalent. Recommendation: Senior or graduate standing or consent of instructor.
4 Credits
A course on mathematical statistics. The emphasis is on theory, though there will also be many computations. Students will analyze problems of estimating, predicting, and inferring given limited data. The major topics include parameter estimation, convergence of random variables, properties of estimators, statistical tests and confidence intervals, and non-parametric statistics.
Four to Six Electives
Category A:
3 Credits
Explore the fundamental concepts of database management systems, including data models, SQL query language, implementation techniques, the management of unstructured and semi-structured data, and scientific data collections.
3 Credits
Delve into the fundamentals of cybersecurity, including attacking and defending networks, searching for vulnerabilities, cryptography, reverse engineering, web security, static and dynamic analysis, malware, and forensics. Hands-on labs and projects are included.
3 Credits
This course will discuss the limits of current web technologies, the similarities and differences between web and software engineering, design, information and service architectures, content management, and testing disciplines. Frameworks such as Rails, Spring, and Symfony will be emphasized and used. Projects will involve search, cloud computing, location-based services, and mobile web development.
Category B:
3 Credits
This course will focus on agents that must learn, plan, and act in complex, non-deterministic environments. We will cover the main theory and approaches of reinforcement learning (RL), along with common software libraries and packages used to implement and test RL algorithms. The course is a graduate seminar with assigned readings and discussions. The content of the course will be guided in part by the interests of the students. It will cover at least the first several chapters of the course textbook. Beyond that, we will move to more advanced and recent readings from the field (e.g., transfer learning and deep RL), with an aim towards focusing on the practical successes and challenges relating to reinforcement learning.
Category C:
3 Credits
This course focuses on the history, theory, and computational methods of artificial intelligence. Basic concepts covered include representation of knowledge and computational methods for reasoning. One or two application areas will be selected and studied from among these topics: expert systems, robotics, computer vision, natural language understanding, and planning.
4 Credits
In this introduction to the study of algorithms, explore strategies that include divide-and-conquer, greedy methods, and dynamic programming. Delve into graph algorithms, sorting, searching, integer arithmetic, hashing, and NP-complete problems.
Capstone Project
DSO 293/294 Capstone
6 Credits
A two-course, hands-on, and project-based culmination to the program, in which students apply data science and analytic principles to the solution of a real-world problem. In the first course, students will perform requirements analysis, review available data sources, and propose a solution strategy to the problem, beginning their analysis. The second course completes the analysis process, culminating in a final report summarizing data gathered, analytic results, lessons learned, and opportunities for future study.
Featured MS in Data Science Faculty
Frequently Asked Questions
The data speaks for itself. The demand for data science jobs is high, meaning compensation offers match the desire for new talent across industries. Many MSDS online graduates earn over $100,000 a year within a few years, so the salary potential with a master’s in data science is promising. Beyond monetary gain, earning your data science master’s degree online can lead to greater, more diverse career opportunities in a field at the forefront of tech innovation.
As a graduate of the Tufts MSDS online program, you can choose from numerous career paths depending on your interests. From data analysis and architecture to modeling and visualization, you’ll have the technical skills to expand your career opportunities and move into management roles. Check out our deep dive into common data science jobs.
The MS in Data Science program welcomes applicants from diverse backgrounds. While a bachelor’s degree is required, there are no specific prerequisites for academic majors. However, a foundational understanding of mathematics, statistics, and programming is beneficial. Students who successfully complete the Pathway to MS in Data Science (post-baccalaureate certificate) will be eligible to enroll in the online MSDS program.
The application requirements for the Tufts Online MSDS program include:
- Application fee
- Resume/CV
- Personal statement
- Transcripts
- Three letters of recommendation
- Official GRE scores (if applicable)
GRE scores not required for applicants who will have received a degree from an institution located in the U.S. or Canada at time of enrollment.
- Official TOEFL, IELTS, or Duolingo test scores (if applicable)
- Portfolio (optional)
For application help, schedule a one-on-one walkthrough with an enrollment advisor.
The 2024-2025 tuition rate for School of Engineering graduate level courses is $1,765 per credit.* Please review tuition and financial aid resources available to online MSDS students.
*While most tuition rate changes are effective in the fall term each academic year, The Trustees of Tufts College reserve the right to change the tuition or to establish additional fees or charges for special features or services whenever such action is deemed advisable. We will provide the earliest possible notification of changes in tuition and other fees.
Yes, Tufts University offers a range of scholarships and financial aid options to support students in their academic pursuits. Reach out to our dedicated financial aid advisors to explore scholarship opportunities, grants, loans, and other forms of financial assistance.
As a student in the Tufts online MSDS program, you’ll have access to a comprehensive array of support services designed to enhance your learning experience and facilitate your academic success. These services include:
- Dedicated academic advisors
- Technical support resources
- Online tutoring
- Career counseling
- Networking opportunities
- Access to online library resources and research databases.
Our goal is to provide you with the support you need to excel in your studies and achieve your professional goals.
No, the Tufts online MSDS program is 100 percent online, allowing you to complete your coursework from anywhere. Our flexible online format enables you to balance your studies with your personal and professional commitments while still benefiting from the same high-quality education and resources available to on-campus students.
To learn more about the Tufts online MS in Data Science program, schedule a one-on-one walkthrough with an enrollment advisor or request more information. Tufts University offers a flexible, 100 percent online program designed to help you advance your career.
Tufts University Accreditation
Tufts University has been continually accredited by the New England Commission of Higher Education (NECHE), one of seven regional higher education accrediting bodies in the United States, since 1929. Tufts is evaluated by and achieves accreditation from NECHE once every ten years. The next comprehensive evaluation will take place in Spring 2033.