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 Combined Degree 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

Course Requirements

Conceptual Foundations of Information and Data (4 Credits - 1 Course)

  • Foundations of Data Analytics (DATA 200)

Application & Professional Competencies (11 Credits - 5 Courses)


  • Communicating with Data (DATA 220) 3 credits
  • Introduction to Python for Data Analysis (DATA 201A) 2 credits – half semester course
  • Python and Machine Learning for Data Analysis (DATA 201B) 2 credits – half semester course
  • Introduction to Data Visualization with Tableau (DATA 202A) 2 credits – half semester course
  • Excel to SQL: Introduction to Data Management and Databases (DATA 202B) 2 credits – half semester course

Statistical Analysis and Modeling Techniques (9 Credits – 3 Courses)

Statistical Analysis

Potential electives:

  • Probability (MATH 165)
  • Statistics (MATH 166)
  • Biostatistics (BIO 132)
  • Computational Geometry (MATH 181 / CS163)
  • Advanced Statistics (EC201)
  • Spatial Statistics – Special Topics (UEP 294)
  • The Mathematics of Poverty and Inequality (MATH 164)
  • Special Topics in Probability and Statistics (MATH 260)
  • Environmental Statistics (ENV 202)
  • Advanced Statistics I (PSYCH 207)
  • Advanced Statistics II (PSYCH 208)
  • Principles of Epidemiology (CEE 154)
  • Numerical Linear Algebra (CS 126/MATH 126)
  • Computational Models of Cognitive Science (CS/PSY 141)
  • Mathematical Aspects of Data Analysis (MATH 123)
  • Real Analysis II (MATH 136)
  • Numerical Analysis (CS 125)
  • Econometrics (EC 202)

Modeling Techniques

Potential electives:

  • Mathematical Psychology (PSYCH 140/PSYCH 240)
  • Environmental Data, Analysis & Visualization (ENV 170)
  • Numerical Analysis (MATH 226 or MATH 126)
  • Numerical Linear Algebra (MATH 128 or MATH 228)
  • Mathematical Aspects of Data Analysis (MATH 123)
  • Introduction to GIS (UEP-232)
  • Advanced GIS (UEP 235)
  • Geospatial Programming Python (UEP 239)
  • Geospatial Modeling (UEP-294)
  • Urban Data Analytics – Special Topics (UEP 294)
  • Ordinary Differential Equations (MATH 153)
  • Partial Differential Equations I (MATH 155 or 255)
  • Partial Differential Equations II (MATH 156 or 256)
  • Special Topics in Differential Equations (MATH 250)
  • Biostatistics (CEE 156)

Discipline-Related Application and Extensions (6 Credits - 2 Courses)

Potential electives:

  • Programming in R for Biologists (BIO 196)
  • Database Systems (CS 115)
  • Big Data (CS 119)
  • Intro to Machine Learning (CS 135)
  • Deep Neural Networks (CS 137)
  • Interactive Web Mapping (UEP 231)
  • Urban Analytics and Visualization (UEP 237)
  • Computational Methods for the Humanities (CLS 160)
  • Principles of Data Science in Python (CS 205)
  • Introduction to Remote Sensing (UEP 189)
  • Advanced Data Analytics (EM 213)

Capstone Internship Experience (3 Credits – 1 Course)

This will provide 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.

Each cohort will have an annual presentation day to share their integrative capstone experience.