Integrated Data Science

What classes do I need to take to get a Certificate in Integrated Data Science?

The Northwestern Certificate in Integrated Data Science requires 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.

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 (winter and spring every year). It will focus on the science motivation and goals that unite three distinct research projects: LSST, LIGO, 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 422 and DATA SCI 423.

ESAM 395/BIOL_SCI-354: Quantitative Biology
This course This course covers some of the landmark results in quantitative biology. Students will learn the biology, mathematics, physics and statistics needed to analyze a variety of data sets acquired from various studies before performing a re-analysis and re-plotting of a central result from these landmark papers. The papers will include studies in gene regulation, developmental biology, sequencing, and more. The course will also include an overview of coding and image analysis, introduction to landmark insights into quantitative biology, random genetic processes, gene expression, cell adaption, cell cycle, developmental morphogens, and phylgenomics.

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 475: 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)

From the Department of Materials Science and Engineering
Atomic Scale Computational Materials Science(MAT_SCI 458)

Students may petition to have an alternate course reviewed for qualification as an elective for this certificate. In order to do this please email:

Completed the IDS Certificate requirements?

In order to petition to have a Graduate Certificate awarded and appear on the transcript, students must submit the Application for a Graduate Certificate once all Graduate Certificate requirements have been completed, but no later than the time that the student files for graduation (in the final quarter of study). Each course counting toward the Graduate Certificate must be listed. The Application for Graduate Certificate requires approval by the Certificate Program Director and, for students also pursuing a PhD or Master’s, the Director of Graduate Study (DGS) of the degree program.