M.S. Humanitarian Engineering and Science: Data Science

Broadly defined, Humanitarian Data Science focuses on applying data science to build more just, diverse, equitable and inclusive worlds.

Humanitarian Data Science spans a wide range of topics, and students may pursue research and practicum projects according to their and their advisor’s particular interests and expertise.

Practitioners work in collaboration with local scientists, communities and government stakeholders.

Application Requirements
  • Degree candidates should have undergraduate coursework in probability, linear algebra and programming. In addition, candidates will need to complete necessary prerequisite courses for the graduate courses, also found in the Mines graduate catalog.
  • Statement of purpose, updated curriculum vitae or resume and transcripts for post-secondary degrees are required for all students.
  • Three letters of recommendation are required for students pursuing the MS thesis option.
  • Non-native English speakers must meet one of the following minimum requirements: TOEFL iBT score of 79; TOEFL paper-based test score of 550; TOEFL computer-based test score of 213; IELTS score of 6.5 or have received a prior degree from an English-speaking university.
  • Mines undergraduate students may count up to six credits from their undergraduate program toward a combined BS/MS degree. External applicants may substitute approved electives with courses brought from elsewhere with written permission from the HES program director.
Required Courses

Engineering, Design and Society courses

  • EDNS 577: Engineering and Sustainable Community Development (3 credits)
  • EDNS 479: Community-Based Research (3 credits)
  • EDNS 580: HES Capstone Practicum (3 credits) — only required for non-thesis/professional master’s degree
  • EDNS 590: Risks in Humanitarian Engineering and Science (3 credits)
  • One elective, such as Introduction to Engineering & Society.

Humanitarian Data Science courses (includes Data Science – Statistical Learning Graduate Certificate)

  • DSCI 403: Introduction to Data Science (3.0 credits)
  • DSCI 530: Statistical Methods (3.0 credits)
  • DSCI 560: Introduction to key statistical learning methods I (3.0 credits)
  • DSCI 561: Introduction to key statistical learning methods II (3.0 credits)
  • One elective from the following pre-approved list — only required for non-thesis/professional master’s degree
    • MATH 432: Spatial Statistics (3.0 credits)
    • MATH 437: Multivariate Analysis (3.0 credits)
    • MATH 498: Special Topics on Time Series (3.0 credits)
    • MATH 536: Advanced Statistical Modeling (3.0 credits)

Students in the MS thesis program should have (a minimum of) 6.0 credits of independent research.

Bolivia Fieldwork