Data Science

For additional information regarding the Data Science minor at Rice University, please see the program’s website: https://datascience.rice.edu

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

Chairs

Frederick L. Oswald
Devika Subramanian

Steering Committee

David Alexander
Rudy Guerra
Matthias Heinkenschloss
Christopher M. Jermaine
Luay K. Nakhleh
Barbara Ostdiek
Kirsten Ostherr
Frederick L. Oswald
Renata Ramos
Devika Subramanian
Marina Vannucci
Ashok Veeraraghavan
Jennifer Wilson

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: Credit cannot be earned for DSCI 301 and 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 (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 305 - DATA, ETHICS, AND SOCIETY

Short Title: DATA, ETHICS, AND SOCIETY

Department: Data Science

Grade Mode: Standard Letter

Course Type: Seminar

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