Course Schedule and Catalog
Course Schedule - Fall Semester 2026
Meeting location information can now be found on student schedules in ESTHER (for students) or on the Course Roster in ESTHER (for faculty and instructors).Additional information available here.
Meeting location information can now be found on student schedules in ESTHER (for students) or on the Course Roster in ESTHER (for faculty and instructors).Additional information available here.
COMP 330 001 (CRN: 16131)
TOOLS & MODELS - DATA SCIENCE
Long Title: TOOLS AND MODELS FOR DATA SCIENCE
Department: Computer Science
Instructors:
Meeting: 4:00PM - 5:15PM MW (24-AUG-2026 - 4-DEC-2026)
Part of Term: Full Term
Grade Mode: Standard Letter
Course Type: Lecture
Language of Instruction: Taught in English
Method of Instruction: Face to Face
Credit Hours: 3
Course Syllabus:
Course Materials: Rice Campus Store
Restrictions:
Must be enrolled in one of the following Level(s):
Undergraduate Professional
Visiting Undergraduate
Undergraduate
Prerequisites: (MATH 211 OR MATH 212 OR MATH 221 OR MATH 222 OR MATH 232) AND COMP 215
Section Max Enrollment: 75
Section Enrolled: 14
Total Cross-list Max Enrollment: 150
Total Cross-list Enrolled: 40
Enrollment data as of: 26-APR-2026 2:28PM
Final Exam: Scheduled Final Exam-OTR Room
Description: This course is an introduction to modern data science. Data science is the study of how to extract actionable, non-trivial knowledge from data. The proposed course will focus both on the software tools used by practitioners of modern data science, as well as the mathematical and statistical models that are employed in conjunction with such software tools. On the tools side, we will cover the basics of relational database systems, as well as modern systems for distributed computing based on MapReduce. On the models side, the course will cover standard supervised and unsupervised models for data analysis and pattern discovery.
Can be taken concurrently with COMP 215. Cross-list: COMP 543. Mutually Exclusive: Cannot register for COMP 330 if student has credit for COMP 543/DSCI 302.