Data Science minor aims to provide students with practical knowledge of the methods and techniques of data analysis, as well as the ability to think critically about the construction and implications of data analysis and models. The minor will empower students across the wide array of campus disciplines with a working knowledge of statistics, probability, and computation that allow students not just to participate in data science projects, but to design and carry out rigorous computational and inferential analysis for their field of interest.
Competencies, Knowledge or Skills to be Achieved
A student who completes the minor will be able to: formulate questions in a domain that can be answered with data; use tools and algorithms from data mining, statistics, applied mathematics, and computer science for analyses; visualize, interpret, and explain results cogently, accurately, and persuasively; understand the underlying social, political, and ethical contexts that are importantly and inevitably tied to data-driven decision-making; be able to connect data to underlying phenomena and to think critically about conclusions drawn from data analysis; be knowledgeable about programming abstractions so that they can later design their own computational inferential procedures; and be well versed with the Python and R programming languages.
Structure of the Minor
Required courses: (12 credit hours) CSCI 205 Introduction to Programming with Python CSCI 350 Introduction to Data Mining STAT 366 Applied Statistics STAT 461 Data Analysis
Electives (3 credit hours) One of the following courses: CSCI 320 Database Design MATH 240 Linear Algebra BANA 201 Applied Business Analytics BIOL 308 Genetics INTL 310 Intelligence Collection Systems and Programs
Total Credit Hours Required 15, at least 9 of which must be completed at The Citadel.