Classes

The Masters in Data Science program will follow a 3 × 3 + 1 design: three modules each consisting of three, 3-credit courses, and a mini-module, comprising 3-credit hours.

Modules


1. Data modeling and statistical learning.
Students will take all three of the following classes.
        • MATH 530 – STATISTICAL METHODS
        • MATH 560 – STATISTICAL LEARNING I (New course sequence)
        • MATH 561 – STATISTICAL LEARNING II

2. Machine learning, data processing and algorithms, and parallel computation.
Students will take all three of the following classes.
        • DSCI 403 (CSCI 303) – INTRODUCTION TO DATA SCIENCE
        • CSCI 470 – INTRODUCTION TO MACHINE LEARNING
        • CSCI 575 – MACHINE LEARNING
              or CSCI563 PARALLEL COMPUTING FOR SCIENTISTS AND ENGINEERS

3. Individualized and domain specific coursework.
Graduate courses in this module will be tailored to each student’s interests and the
suitability will be managed by the program curriculum committee.

Two possible examples from geoscience and engineering are:
• Geophysics (Contact Paul Sava)
        GPGN 533 – GEOPHYSICAL DATA INTEGRATION & GEOSTATISTICS.
        GPGN 570 – APPLICATIONS OF SATELLITE REMOTE SENSING.
        GPGN 605 – INVERSION THEORY.
• Electrical Engineering (Contact Michael Wakin) 3 out the following 4 courses:
        EENG 411 – DIGITAL SIGNAL PROCESSING.
        EENG 511 – CONVEX OPTIMIZATION AND ENG. APPLICATIONS.
        EENG 515 – MATHEMATICAL METHODS FOR SIGNALS AND SYSTEMS
        EENG 519 – ESTIMATION THEORY AND KALMAN FILTERING

Mini-Modules


Professional skills and career development
Students will take one of the following courses.
    • SYGN502 INTRODUCTION TO RESEARCH ETHICS
    • SYGN598 LEADERSHIP AND TEAMWORK
    • LICM501 PROFESSIONAL ORAL COMMUNICATION