Integrated Data Science Certificate Requirements

The following requirements are in addition to, or further elaborate upon, those requirements outlined in The Graduate School Policy Guide.

To complete the Integrated Data Science (IDS) Certificate requirements, students will take five courses including at least one course from group A, at least two courses from group B, at least one course from group C, and a fifth course from any group. The courses currently available in each curriculum group are described below, developed in connection to the NSF IDEAS traineeship.

Group A. Data Challenges in Domain Disciplines

DATA SCI-401: Data-Driven Research in Physics, Geophysics, and Astronomy
This course will integrate the domain-focused projects in P&A (Physics & Astronomy) and EPS (Earth and Planetary Sciences) and will be team-taught by one professor from P&A and one from EPS. This course will cover one quarter of material, but be spread over 2 quarters (Fall and Winter every year). It will focus on the science motivation and goals that unite three distinct research projects: LSST, aLIGO, and EarthScope. It will focus on principles and methods of data analysis. Spreading the course over two quarters will allow alignment and further interdisciplinary integration with DATA SCI 421 and DATA SCI 422.

Group B. Core Data Analytics

DATA SCI -421: Integrated Data Analytics I (cross-listed as PHYS 441: Statistical Methods for Physicists and Astronomers)
DATA SCI -422: Integrated Data Analytics II (cross-listed as EPS 329: Mathematical Inverse Methods in Earth and Environmental Sciences)
DATA SCI -423: Integrated Data Analytics III (cross-listed as EECS 495: Machine Learning: Foundations, Applications, and Algorithms)

Group C. Electives in Data Analytics

From the Department of Electrical Engineering and Computer Science (McC):

  • Data Management and Information Processing (EECS 317)
  • Machine Learning (EECS 349)
  • Digital Image Processing (EECS 420)
  • Nonlinear Optimization (EECS 479)
  • Probabilistic Graphical Models (EECS 395/495)
  • Statistical Pattern Recognition (EECS 433)
  • Social Media Mining (EECS 510)
  • Geospatial Vision and Visualization (EECS 395/495)
  • Data Science (EECS 395/495)

From the Department of Engineering Sciences and Applied Mathematics

  • Models in Applied Mathematics (ES_APPM 421-1)
  • Numerical Methods for Random Processes (ES_APPM 448)

From the Department of Statistics:

  • Time Series Analysis (STAT 454)
  • Applied Bayesian Inference (STAT 457)
  • Theory of Data Mining (STAT 461)

From the Department of Industrial Engineering and Management Sciences:

  • Statistical Methods for Data Mining (IEMS 304)