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 375-1: What Does the Data Say?
Mirroring the manner in which computation revolutionized our study of differential equations (we now use computers to simulate the flow of air over an aerofoil, rather than compute by hand), the near-universal access to powerful computation calls for a new era of statistical assessment. Students need not carry around a list of xyz tests, and throw the kitchen sink at their data. Instead they can use a combination of a few simple ideas and the power of their computers to assess the confidence or surprise, they have in their results.

The course has 3 broad sections: 1) The basics, 2) When you have a model in mind, and 3) When you don’t have a model at hand. The overall theme running through the class is to assess confidence through constructing quantitative expectations in your computer.
This course is not focused on biological phenomena or biological datasets. We use data from biology only because it is readily accessible to the teaching faculty.
Further Information from Professor Madhav Mani: “What Does the Data Say?”

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 ELEC_ENG 475: Machine Learning: Foundations, Applications, and Algorithms)

Group C. Electives in Data Analytics

From the Department of Chemical and Biological Engineering:
Computational Biology: Principles and Applications (ChE 379)

From the Department of Computer Science:
Design and Analysis of Algorithms (COMP_SCI 336)
Data Science (COMP_SCI 496)
Human-Centered Machine Learning (COMP_SCI 496)
*please note that students that have taken Machine Learning (formerly EECS 349, now COMP_SCI 349) prior to Winter 2020 are able to count this toward elective credit

From the Department of Electrical and Computer Engineering:
Digital Image Processing (formerly EECS 420, now ELEC_ENG 420)
Statistical Pattern Recognition (formerly EECS 433, now ELEC_ENG 433)
Deep Learning Foundations from Scratch (ELEC_ENG 435)
Deep Reinforcement Learning (formerly EECS 395/495, now ELEC_ENG 473)
Geospatial Vision and Visualization (formerly EECS 395/495, now ELEC_ENG 395/495)
Social Media Mining (formerly EECS 510, now COMP_ENG 510)

Formerly Offered in the Department of Electrical Engineering and Computer Science (EECS):
Nonlinear Optimization (EECS 479)
Probabilistic Graphical Models (EECS 395/495)
Data Science (EECS 395/495)

From the Department of Statistics:
Regression Analysis (STAT 350)
Intro to Analysis of Financial Data (STAT 365)
Time Series Analysis (STAT 454)
Applied Bayesian Inference (STAT 457)
Advanced Topics: Theory of Data Mining (STAT 461)
Advanced Topics: Bayesian Statistics (STAT 461)

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 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.