Data Science

Christopher M. Jermaine
Program Director
christopher.m.jermaine@rice.edu

Rudy Guerra
Minor Advisor
rguerra@rice.edu

The minor in Data Science is an interdisciplinary undergraduate program administered by the George R. Brown School of Engineering. 

The Data Science minor curriculum emphasizes doing data science and aims to teach students best practices in the field. Students learn technical competencies by taking core courses in statistics, computer science, and machine learning. This knowledge base is complemented with courses that inform the student of the broader impact of the information age on human activity, including discussions on data privacy, ethics, reproducibility, communication, decision-making, and data visualization. This program culminates with a capstone experience whereby students work in teams to complete a semester-long data science project selected from a variety of disciplines and industries. The curriculum is summarized in terms of four foundational competencies: quantitative, communications, ethics, and substantive application. 

Data Science does not currently offer an academic program at the graduate level.

Co-Chairs

Frederick L. Oswald, Psychological Sciences
Devika Subramanian, Computer Science, Electrical and Computer Engineering

Program Director

Christopher M. Jermaine, Computer Science

Minor Advisor

Rudy Guerra, Statistics

Steering Committee

David Alexander, Physics and Astronomy
Rudy Guerra, Statistics
Matthias Heinkenschloss, Computational and Applied Mathematics
Christopher M. Jermaine, Computer Science
Luay K. Nakhleh, Computer Science, Biochemistry and Cell Biology
Barbara Ostdiek, Finance and Statistics
Kirsten Ostherr, English
Frederick L. Oswald, Psychological Sciences
Renata Ramos, Bioengineering
Devika Subramanian, Computer Science, Electrical and Computer Engineering
Marina Vannucci, Statistics 
Ashok Veeraraghavan, Electrical and Computer Engineering
Jennifer Wilson, Program in Writing and Communication

For Rice University degree-granting programs:
To view the list of official course offerings, please see Rice’s Course Catalog
To view the most recent semester’s course schedule, please see Rice's Course Schedule

DSCI 301 - PROBABILITY AND STATISTICS FOR DATA SCIENCE

Short Title: STATISTICS FOR DATA SCIENCE

Department: Data Science

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Distribution Group: Distribution Group III

Credit Hours: 4

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

Prerequisite(s): MATH 102 or MATH 106 or MATH 112

Description: An introduction to mathematical statistics and computation for applications to data science. Topics include probability, random variables expectation, sampling distributions, estimation, confidence intervals, hypothesis testing and regression. A weekly lab will cover the statistical package, R, and data projects. Cross-list: STAT 315. Recommended Prerequisite(s): MATH 212. Mutually Exclusive: Cannot register for DSCI 301 if student has credit for ECON 307/STAT 310.

DSCI 302 - INTRODUCTION TO DATA SCIENCE TOOLS AND MODELS

Short Title: DATA SCIENCE TOOLS AND MODELS

Department: Data Science

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

Prerequisite(s): COMP 140 and (DSCI 301 or ECON 307 or STAT 310 or STAT 315)

Description: This course introduces key concepts in data management, preparation, and modeling and provides students with hands-on experience in performing these tasks using modern tools, including relational databases and Spark. Models covered include linear and logistic regression and gradient descent. For registration purposes, COMP 140 is a required prerequisite for this course. With instructor permission, students that have taken CAAM 210 (or another applicable course) may be allowed to special register for this course. Students seeking this instructor permission (to waive or substitute the COMP 140 prerequisite requirement) are expected to know the Python programming language, and may be required to demonstrate proficiency.

DSCI 303 - MACHINE LEARNING FOR DATA SCIENCE

Short Title: MACHINE LEARNING FOR DS

Department: Data Science

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

Prerequisite(s): DSCI 301 and DSCI 302

Description: This course is an introduction to concepts, methods, best practices, and theoretical foundations of machine learning. Topics covered include regression, classification, kernels, dimensionality reduction, clustering, decision trees, ensemble learning, regularization, learning theory, and neural networks. Recommended Prerequisite(s): CAAM 334 or CAAM 335 or MATH 355 Mutually Exclusive: Cannot register for DSCI 303 if student has credit for ELEC 478/ELEC 578.

DSCI 304 - INTRODUCTION TO EFFECTIVE DATA VISUALIZATION

Short Title: DATA VISUALIZATION

Department: Data Science

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Credit Hours: 3

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

Prerequisite(s): (DSCI 301 or ECON 307 or STAT 310 or STAT 315) and DSCI 302 (may be taken concurrently)

Description: This course teaches fundamental data visualization skills to undergraduate students in the Data Science minor. Students will learn how to create data visualizations in Python or R, how to design effective visualizations that account for visual perception, and how to explain and present data to technical and non-technical audiences.

DSCI 305 - DATA, ETHICS, AND SOCIETY

Short Title: DATA, ETHICS, AND SOCIETY

Department: Data Science

Grade Mode: Standard Letter

Course Type: Seminar

Distribution Group: Distribution Group II

Credit Hours: 3

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

Description: An examination of the ethical implications and societal impacts of choices made by data science professionals. The course will provide practical guidance on evaluating ethical concerns, identifying the potential for harm, and applying best practices to protect privacy, design responsible algorithms, and increase the societal benefit of data science research.

Description and Code Legend

Note: Internally, the university uses the following descriptions, codes, and abbreviations for this academic program. The following is a quick reference:  

Course Catalog/Schedule 

  • Course offerings/subject code: DSCI

Program Description and Code

  • Data Science: DSCI

Undergraduate Minor Description and Code

  • Minor in Data Science: DSCI

CIP Code and Description1

  • DSCI Minor: CIP Code/Title: 27.0304 - Computational and Applied Mathematics