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Course Descriptions


The entire University of Scranton Course Catalog is available on the University of Scranton website



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DS 201 - Introduction to Data Science

Prerequisites:Math placement PT score 14 or higher, or permission of instructor
Corequisites:None
Credits:3cr

An introduction to basic data science workflow following current best practices. This course will introduce students to computational or algorithmic ways to think about and learn from data. Emphasis will be placed on data visualization, exploratory data analysis, and foundational modeling principles and techniques implemented using an appropriate programming language.

DS 210 - Mathematical Methods for Data Science

Prerequisites:MATH 221
Corequisites:None
Credits:3cr

This course provides a concise overview of certain mathematical methods that are essential in data science. The primary methods to be covered should come from probability and statistics, networks and graph theory, and optimization. Additional data science relevant topics may be covered at the discretion of the instructor.

DS 362 - Data-Driven Knowledge Discovery

Prerequisites:(CMPS 240 and DS 201 and DS 210) or instructor's permission
Corequisites:None
Credits:3cr

This course covers the process of knowledge discovery including data selection, pre-processing, transformation, data mining, evaluation, and validation, with an emphasis on data mining concepts, algorithms, and techniques for common tasks such as association rule learning, classification, regression, clustering, and outlier detection.


Course Descriptions

The entire University of Scranton Course Catalog is available on the University of Scranton website





Pre-requisites:Math placement PT score 14 or higher, or permission of instructor
Co-requisites:None
Credits:3cr

Pre-requisites:MATH 221
Co-requisites:None
Credits:3cr

Pre-requisites:(CMPS 240 and DS 201 and DS 210) or instructor's permission
Co-requisites:None
Credits:3cr