MS in Data Analytics
The 10 course interdisciplinary Master of Science in Data Analytics provides students with the skills needed to fill the increasing demand for individuals who can gather, interpret and guide data-driven decisions. The program is guided by an advisory board of professionals in the field, who help ensure the program provides rigorous courses in the analytical skills that are sought after across the arts, humanities, and sciences as well as within 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
- 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
- Maintain collaborative team relationships to effectively contribute to a shared project
- Have functional programming skills in data-related language
Required Core Courses
- Foundations of Data Analytics (DATA 200) 4 credits
- Introduction to Python and Machine Learning (DATA 201) 4 credits
- Database Design and SQL (DATA 202) 4 credits
- Communicating with Data (DATA 220) 4 credits
- Capstone Internship Experience (DATA 299) 3 Credits
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 electives
- 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.