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 

  • Conceptual Foundations of Information and Data (DATA200) 4 Credits 
  • Communicating with Data (DATA 220) 3 Credits  
  • Introduction to Python and Machine Learning (DATA 201) 4 credits 
  • Introduction to Data Visualization with Tableau (DATA 202A) 2 credits – half semester course 
  • Database Design and SQL (DATA 202B) 2 credits – half semester course 
  • Capstone Internship Experience (DATA 299) 3 Credits 

Elective Courses  

(5 courses – at least 15 credits) with at least two courses from each the following two 
areas: Statistical Analysis and Modeling Techniques and Discipline-Related Application 
 
Approved electives for each semester can be found on the Data Analytics website on Canvas 
 
Examples of Statistical Analysis and Modeling Technique electives  

  • Probability (MATH 165) 
  • Computational Models of Cognitive Science (CS 134/PSY 141) 
  • Econometrics (EC 202) 
  • Introduction to GIS (UEP-232)  

Examples of Discipline-Related Application electives

  • Intro to Machine Learning (CS 135) 
  • Deep Neural Networks (CS 137) 
  • Bayesian Deep Learning (CS 152) 
  • Urban Analytics and Visualization (UEP 237)Capstone Internship Experience (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.