Minors

College of Science and Mathematics
Department of Computer and Computational Sciences

The Department currently offers two minors: Computational Sciences and Data Science

Computational science is an interdisciplinary field that combines mathematical and computing methods for solving complex real-world scientific, financial or societal problems through modeling, simulation, optimization, or visualization methods. This Computational Science minor offers students opportunities to study and apply scientific and mathematical techniques in various application fields. The minor in Computational Science will prepare students to solve complex problems by completing computational based projects that require intensive computational processes and high-performance computing tools.

Note: Computational Science should not be confounded with Computer Science which is the study of the theoretical foundations of information and computation, and of practical techniques for their implementation and application in computer systems.

In addition to the General Education prerequisites, students must complete 23-26 credits with an average grade of C or higher.

Required courses	Prerequisites	Credits
CSC118 Programming II	CSC117	4
CSC242 Data Structure	CSC118	4
CSC 239 Scientific Computing	MAT 140 or MAT143	3
MAT261 Linear Algebra	MAT 241 (may be take concurrently)	4
Total		14

Select one of the following	Prerequisite	Credits
MAT325 Numerical Analysis	MAT261 (can be taken concurrently) and CSC117 (or knowledge of a programming language)	4
Or
MAT 352 Mathematical Modeling	MAT261(may be taken concurrently)	3
Total		3-4

Computational Science Minor Electives

Select at least 6 credits of the following:	Prerequisites	Credits
CSC317: Programming III	CSC242	3
CSC465: High Performance Computing (currently offered as a selected topic)*	CSC242	3
CSC361: Bioinformatics	BIO142 and CSC118 and MAT153, and either (BIO 245 and BIO 223) or (8 credits of 200-level CSC courses) or (MAT 233 and MAT 261).	4
MAT352 Mathematical Modeling**	MAT 261	3
MAT325 Numerical Analysis**	MAT 261 and CSC 117	4
MAT346 Differential Equations	MAT 342 (may be taken concurrently)	4
CHE 341 Physical Chemistry I	MAT 242, and PHY 241	1
CHE 342 Physical Chemistry II	CHE 341	1
CSC466: Selected topic in Data Mining	CSC 332 or CSC 245	3
BIO 465, CHE465, MAT465, MBI465, PHY465-Selected topic in computational science as approved by the Chair of Computer and Computational Science in consultation with the Chair of the department of the student's major. Approval will be based on the coherence of the selected courses in preparing the student for work in a particular interdisciplinary area		2-4
SCI 497: Interdisciplinary Capstone seminar	BIO 397-398 or CHE 397-398 or CSC 397-398 or MAT 397 or MBI 397-398.	1
Total		6-8

The minor in Data Science affords students the opportunity to extend their quantitative abilities as a route to a deeper understanding of their chosen field and to greater marketability after graduation. Students must complete the following courses with a passing grade in each course with 18-20 credits.

A. Required courses	Prerequisites	Credits
SCI/CSC 230 Data Science I Data Science I provides students with an introduction to the concepts and basic skills needed to understand the role of data in today’s world. The course explores the emergence of the field using the data science workflow as the unifying framework to illustrate the importance of each stage of the workflow, how it contributes to the final report, and how that new information is used. Topics include applications of data science; data ethics; data preparation; data stewardship; analysis, evaluation, communicating results, and best practices. The trade-offs among tools, algorithms, and visualizations are discussed using both effective and ineffective examples. This is a hands-on course, students work with datasets in peer-peer and near-peer groups.	MAT 140 or MAT 143 or for students with higher standing any other higher level math course	3
CSC 239 Scientific Computer Applications	MAT 140 or MAT143	3
SCI/CSC/IST 435 Data Science II SCI 435 Data Science II provides students with the core competencies in data science in preparation for graduate studies or an entry-level position in data science. The course builds on the fundamental concepts of data science with real-world examples that require advanced mathematical, statistical, programming, and critical thinking skills. This is a hands-on course, students will work with multiple datasets for their assignments. The course is suitable for upper-level undergraduate students in computer science and computational sciences, applied mathematics, business, and related analytical fields. 3 credits.	SCI/CSC 230, MAT 235 or MAT 245 or DSC 325.	3

A. Required courses

Prerequisites

Credits

SCI/CSC 230 Data Science I

Data Science I provides students with an introduction to the concepts and basic skills needed to understand the role of data in today’s world. The course explores the emergence of the field using the data science workflow as the unifying framework to illustrate the importance of each stage of the workflow, how it contributes to the final report, and how that new information is used. Topics include applications of data science; data ethics; data preparation; data stewardship; analysis, evaluation, communicating results, and best practices. The trade-offs among tools, algorithms, and visualizations are discussed using both effective and ineffective examples. This is a hands-on course, students work with datasets in peer-peer and near-peer groups.

MAT 140 or MAT 143 or for students with higher standing any other higher level math course

CSC 239 Scientific Computer Applications

MAT 140 or MAT143

SCI/CSC/IST 435 Data Science II

SCI 435 Data Science II provides students with the core competencies in data science in preparation for graduate studies or an entry-level position in data science. The course builds on the fundamental concepts of data science with real-world examples that require advanced mathematical, statistical, programming, and critical thinking skills. This is a hands-on course, students will work with multiple datasets for their assignments. The course is suitable for upper-level undergraduate students in computer science and computational sciences, applied mathematics, business, and related analytical fields. 3 credits.

SCI/CSC 230, MAT 235 or MAT 245 or DSC 325.

B. Statistics requirement. The student must choose any one of the following courses.	Prerequisites	Credits
MAT 235 Introductory Statistics with Applications	MAT 140 or 143 or satisfactory scores on department diagnostic examinations.	4
DSC 325	MAT 140 or MAT143	3
MAT 245	SCI/CSC 230, MAT 235 or MAT 245 or DSC 325.	3

C. Data Application requirement. The student must choose one of these courses.	Prerequisites	Credits
IST 305 Database Design and Implementation	IST 301, IST 305	3
CSC 245 Databases and Information Retrieval	CSC 241, CSC242	3
DSC 410 Quantitative Methods Introduction	DSC 325	4
SSC 228 Quantitative Research Methods	SSC 227	3
SSC 220/SCI 220/CJU 220 Introduction to Geographic Information Systems	None	3
BIO/CSC/MAT 361 Bioinformatics	BIO 141, MAT 143 and either CSC 110 or CSC 117	4
MAT352 Mathematical Modeling	MAT 261	3
CSC 466 Data Mining	CSC 245	3

*D. Complete a data science related project in one of the following courses.	Prerequisites	Credits
xxx 495 Directed Independent Research	SCI/CSC/IST 4xx	3
MAT 499 Independent Study	Permission of a full-time faculty member and approval of the Mathematics Coordinator.	3
BUS 499 Independent Study	Senior standing	3
IST 425 Project Management and Development II	IST 420 and senior standing.	3
MKT 430 Strategic Marketing	MGT 301, MKT 301, MKT 334 and MKT 426.	3

* Department Chairs are responsible for ensuring that projects relate to data science.

For More Information Contact

Title	Name
Dean	Sandra Romano Ph.D.
Department Chair	Marc Boumedine, Ph.D.
Program Coordinator	Aaron Rapp, Ph. D . aaron.rapp@uvi.edu
Administrative Assistant	tennika.williams@uvi.edu

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