Data Science (DSCI)

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.