Applied Data Science at CWRU

Remote Data Science Courses for Undergraduate and Graduate Students attending partner Universities

Joint Undergraduate and Graduate Courses

At Case Western Reserve University, Dr. Roger French has developed a groundbreaking Applied Data Science curriculum with an accompanying model for providing foundational data science curriculum to partnering institutions at no cost. As the Kyocera Professor in Materials Science and Engineering with a Secondary Appointment in Computer and Data Science, French has developed an innovative curriculum that taps into an area of significant research interest, equipping students with a unique competitive edge that cuts across many industries.

Whether innovating for the future or working to improve the decision-making process, data lies at the heart of the challenge. With rapid digitalization, the volume of global data is growing exponentially and many industries rely on data-driven insights. 

  • Data forms the background of the financial services industry, advertisers use data to decide where to invest their marketing dollars and product designers use data-driven insights to develop new products that are aligned with customer expectations. 

  • In healthcare, data visualization is helping to substantially improve patient outcomes.

  • And in the energy sector, data science is allowing for efficiency increases and advances in material science. 

Through this partnership with Case Western Reserve, students can acquire in-demand skills as well as the opportunity to engage with exciting research within the university.

As the institution offering the curriculum, Case Western Reserve University provides:

  • Structured course content which can be conducted remotely and synchronously in two to three semesters;

  • Teaching Assistant support for the first student cohort, after which Teaching Assistant responsibilities may be transitioned to students at the partner institution that have completed the curriculum;

  • Free of charge—no funding obligation for the partner institution.


Students must have completed an introductory course in computer programming. Students without previous computer programming experience are able to enroll in a Spring 2022 introductory course with Dr. Laura Bruckman. Enrollment in this preliminary course is also provided free of charge to our partner institutions.

Fall 2022 Semester

DSCI351/DSCI451: Exploratory Data  Science

Students utilize data science and analysis approaches to identify statistically significance relationships and better model and predict the behavior of these systems; assemble and explore real-world datasets, perform clustering and pair plot analyses to investigate correlations, and employ logistic regression to develop associated predictive models; interpret, visualize and discuss results; introduce basic elements of statistical analysis using R Project open source software for exploratory data analysis and model development. Course includes an introduction to R data types, reading and writing data, looping, plotting and regular expressions.

Spring 2023 Semester

DSCI353/DSCI453: Data Science: Statistical Learning, Modeling and Prediction

Students use an open data science tool chain to develop reproducible data analyses useful for inference, modeling and prediction of the behavior of complex systems; identify statistically significant relationships from datasets derived from population samples, and infer the reliability of these findings in addition to the standard data cleaning, assembly and exploratory data analysis steps essential to all data analyses; use regression methods to model a number of both real- world and lab-based systems producing predictive models applicable in comparable populations; assemble and explore real-world datasets, use pair-wise plots to explore correlations, perform clustering, self-similarity, and logistic regression develop both fixed-effect and mixed-effect predictive models; introduce machine-learning approaches for classification and tree-based methods. Results will be interpreted, visualized and discussed. We will introduce the basic elements of data science and analytics using R Project open source software.

For more information on this program, please contact Jonathan Steirer at 216.368.0374 or

Interested students should contact their university’s registrar to determine availability of this program.