INTRODUCTION TO DATA SCIENCE TOOLS AND MODELS
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, pandas, and Spark. Models covered include kNearest Neighbors, linear regression and gradient descent. For registration purposes, DSCI 101 or COMP 140 is a required prerequisite for this course. With instructor permission, students who have experience with the Python programming language may be allowed to special register for this course. Note that these students may be required to demonstrate proficiency with Python. Priority for this course is given to students enrolled in the data science minor or sport analytics major. Other students may be permitted to enroll at the discretion of the instructor. Mutually Exclusive: Cannot register for DSCI 302 if student has credit for COMP 330 .
Prerequisites & course information
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 students with a major in E, E, E or Sport Analytics. Enrollment is limited to students with a minor in E, E, E or Data Science. Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students. Students in the Computer Science department may not enroll. Course Level: Undergraduate Upper-Level Prerequisite(s): COMP 140 or DSCI 101
Sources
- Course catalogue ↗2026–2027