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KSAWorks
Knowledge • Skills • Abilities
University of Scranton · Courses

DS

3 courses with the subject DS, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.

DS 201(Q) Introduction to Data Science3

(Prerequisite: Math Placement PT score of 14 or higher, or ALEKS score of 76 or higher, or MATH 114 , or permission of instructor) 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.

Subject
DS
Credits (min)
3
Credits (max)
3
Credit unit
cr.
Type
course
Edition
2026-2027
Catalog
Undergraduate Catalog 2026-2027
Source
catalog.scranton.edu
Prerequisite
Math Placement PT score of 14 or higher, or ALEKS score of 76 or higher, or MATH 114 , or permission of instructor) 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 210Mathematical Methods for Data Science3
Subject
DS
Credits (min)
3
Credits (max)
3
Credit unit
cr.
Type
course
Edition
2026-2027
Catalog
Undergraduate Catalog 2026-2027
Source
catalog.scranton.edu
Prerequisite
MATH 221 ) 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 362Data-Driven Knowledge Discovery3
Subject
DS
Credits (min)
3
Credits (max)
3
Credit unit
cr.
Type
course
Edition
2026-2027
Catalog
Undergraduate Catalog 2026-2027
Source
catalog.scranton.edu
Prerequisite
CMPS 240 and DS 201 and DS 210 ) 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.

Source: University of Scranton's catalog, linked per course · table learning_unit · CourseShelf publish 59