MS in Data Analytics

The 10-course, interdisciplinary Master of Science in Data Analytics provides students with the skills needed to meet the growing demand for professionals who can gather, interpret, and guide data-driven decision-making. The program integrates generative AI as a companion throughout the core curriculum and engages students with real-world datasets through hands-on projects, emphasizing experiential learning and the practical application of analytical skills. Guided by an advisory board of professionals in the field, the program ensures that students develop rigorous, relevant skills that are sought after across the arts, humanities, and sciences, as well as throughout the business community.

Most full-time students complete the program in two years, but it can be completed in one year with accelerated study. There is also a Fifth-Year Master's Degree Program option for Tufts undergraduate students.

Upon completion of the program, you will be able to demonstrate the following areas of knowledge:

• Define and solve complex data-based problems using appropriate statistical methodologies analyses
• Demonstrate the ability to apply data analytics skills to real-world problems using authentic, messy datasets
• Select appropriate statistical and predictive methodologies with both sparse and large data sets
• Provide appropriate theoretical interpretation of these results based on discipline-related concepts
• Demonstrate written oral communications skills for conclusions drawn from the analyses of data
• Create visual representations to increase understanding and utilization of complex data
• Have functional programming skills in data-related language
• Use generative AI to support coding and debugging while critically evaluating and validating its output
• Maintain collaborative team relationships to effectively contribute to a shared project

Required Core Courses 

• Data Analytics and Machine Learning with Python (4 credits)
• Database Design and SQL (4 credits)
• Data Analytics with R (4 credits)
• Communicating with Data (4 credits)
• Capstone Internship Experience (3 Credits) 

Elective Courses  

Five courses (15 credits minimum) with at least two courses from each of the following two areas: Statistical Analysis and Modeling Techniques and Discipline-Related Applications. Approved electives for each semester can be found on the Data Analytics Canvas page. 

Examples of possible Statistical Analysis and Modeling Technique elective:

• Probability (MATH 165)
• Computational Models of Cognitive Science (CS 134/PSY 141)
• Econometrics (EC 202)
• Biostatistics (CEE 156) 

Examples of possible Discipline-Related Application electives:

• Introduction to Machine Learning (CS 135)
• Big Data (CS 119)
• Bayesian Deep Learning (CS 152)
• Introduction to GIS (UEP 232)

The Capstone Internship (3 credits - 1 course)

The Capstone Internship Experience provides students with the opportunity to apply the knowledge and problem-solving skills they've learned over with course of the program with real-world projects or data sets within a professional context. This is envisioned to be accomplished during either the summer or academic year and is critical to the building of the problem-solving soft skills required by employers.