MS in Data Science

CS119 Big Data

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.

CS135 Introduction to Machine Learning

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.

EE 104 Probabilistic System Analysis

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.

MATH 166 Statistics

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.

Category A:

CS 115 Database Systems

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.

CS 116 Introduction to Security

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.

CS 120 Web Programming and Engineering

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:

CS 138 Reinforcement Learning

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:

CS 131 Artificial Intelligence

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.

CS 160 Algorithms

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.

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.