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Approved Courses Examples of Course Selection Pathways

Approved Courses

For students enrolled prior to 2024, participation in at least 7-9 data-science specific seminars (1 semester) to enrich their formal didactic training is required. These seminar series could be from different schools, Institutes, Initiatives, Centers, etc. Seminar attendance should be recorded on this log.

Click on the button below to view all courses approved for the Graduate Data Science Certificate. This list is sorted by course code. You may use temporary filters to view Foundation courses, Applications courses and Electives, and sort the list.

Approved Course list

Don’t see a course you think would fit? Request approval for a course for your certificate.

Examples of Course Selection Pathways

Below are examples of possible curricular paths through the Graduate Certificate for Data Science program based on a student’s prior experience. This is intended to illustrate, not limit, the scope of candidates, or courses they can take to complete the program and obtain the certificate.

  • Any field, no programming or statistics experience
    • SI 506 Programming I (Applications)
    • SI 507 Intermediate Programming (Applications)
    • SI 544 Introduction to Statistics (Foundations)
  • Any field, some self-taught programming (like the Python 3 series), no statistics experience
    • SI 507 Intermediate Programming (Applications)
    • SI 544 Introduction to Statistics (Foundations) 
    • SI 618 Data Manipulation & Analysis (Applications/Foundations) 
  • For students with some programming and undergraduate math experience, grouped by research interests:
    • Earth/atmosphere and geosciences
      • SPACE 423: Data Analysis and Visualization for Geoscientists (Foundations)
      • BIOSTAT 696: Spatial Statistics (Applications/Foundations)
      • ENVIRON 473: Statistical Modeling and Data Visualization in R (Applications)
    • Health sciences
      • LHS 610: Exploratory Data Analysis for Health (Foundations)
      • BIOSTAT 521: Applied Biostatistics I (Foundations/Applications)
      • EPID 633: Introduction to Mathematical Modeling (Applications)
    • Biological sciences
      • STATS 415: Data Mining and Statistical Learning (Foundations)
      • BIOINF 597: Artificial Intelligence for Medicine and Biomedical Sciences (Applications)
      • EECS 545: Machine Learning (Foundations/Applications)
    • Education
      • LHS 631: Learning Analytics: Foundations and Applications (Electives)
      • SI 618: Data Manipulation & Analysis (Foundations)
      • SI 630: Natural Language Processing: Algorithms and People (Applications)
    • Humanities 
      • SI 630: Natural Language Processing: Algorithms and People (Foundations)
      • SI 649: Information Visualization (Electives)
      • SI 608: Networks: Theory and Application (Applications)
    • Materials sciences & engineering
      • EECS 545: Machine Learning (Foundations/Applications)
      • CSE 549: Information Retrieval (Foundations/Applications)
      • PHYSICS 514: Computational Physics (Electives)
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