Data Sciences Minor

The minor in Data Sciences teaches principles and builds skills in the science of how and why we use data. It is an attractive option that can enhance the credentials for students in a wide variety of disciplines.  Decision making across all levels is increasingly shifting away from subjective human judgment and expert opinion and is being replaced by superior evidence-based approaches driven by data and analytical models. Data Sciences is the discipline concerned with developing and applying analytical models and methods to gain critical insights from data, understand the behavior of complex systems, and make non-trivial decisions optimally, often in response to quickly changing conditions.  Businesses and scientists alike use the techniques of this field to perform computational simulations in a multitude of areas where actual experiments are impractical or impossible.  The minor in Data Sciences is designed to provide students with the core fundamental coursework in mathematics, computer science, and business to succeed in this area.

Requirements for Data Sciences Minor

Twenty-three (23) credits to include Mathematics (MATH) 200; MATH 300; Computer Science (CPSC) 220; CPSC 419; CPSC 420; 4 elective credits from among CPSC 230 or Business Administration (BUAD) 400; 3 elective credits from among BUAD 403 or CPSC 425. Note that MATH 121 and 122 are prerequisites to MATH 300, and either MATH 201 or CPSC 125 is a prerequisite to MATH 300. Students should bear this in mind when planning their academic coursework.

Business Administration Course Offerings for the Data Sciences Minor

Business Administration course offerings will be found under the 4 letter code of CPSC in the course listings.

400 – Analytics Application Development (4)

Prerequisite: Grade of C or better in CPSC 220 or equivalent. A course in programming and data manipulation techniques for constructing analytics-based applications. Topics include SQL and no-SQL databases, using web service API’s to acquire data, introduction to Hadoop and MapReduce, and use of third-party analytic component API’s.

403 –Foundations and Applications of Data Analytics (3)

Prerequisite: Grade of C or better in CPSC 220 or equivalent. This course develops an overview of the challenges of developing and applying analytics for insight and decision-making. Examples and cases will come from customer relation management, price modeling, social media analytics, location analysis and other business domains.

Computer Science Course Offerings for the Data Sciences Minor

Computer Science course offerings will be found under the 4 letter code of CPSC in the course listings.

220 – Computer Science I (4)

Prerequisite: CPSC 110 or permission of instructor. Continued coverage of disciplined problem-solving and algorithmic development including emphasis on procedural and data abstraction. Topics include elementary data structures such as arrays, files, and classes. The notions of data modeling and the linking of data type definitions with their associated operations is introduced. Study of program design, coding, debugging, testing, and documentation in a higher level language that supports the object-oriented paradigm. Intended for students who have had previous programming experience.

230 – Computer Science II: Data Structures (4)

Prerequisite: Grade of C or better in CPSC 220. Continued study of data modeling and incorporation of abstract data types including linked lists, stacks, queues, heaps, trees, and graphs. Study of advanced sorting and searching techniques. Provides experience in the use of algorithm analysis. Continued study of program design, coding, debugging, testing, and documentation in an object-oriented higher level language.

419 – Data Mining (3)

Prerequisites: CPSC 220. Practical knowledge of data mining and information retrieval. Students will examine the theoretical foundations of a variety of techniques, gain experience with these techniques using open source software, and learn how to apply them to real-world problems. Topics include decision trees, Naïve Bayes, Probabilistic retrieval models, clustering, support vector machines and approaches to web mining.

420 – Modeling and Simulation (3)

Prerequisite: CPSC 220. A robust introduction to techniques of mathematical modeling and computational simulation applied to practical problems. Topics include system dynamics approaches, discrete-event simulation, and agent-based models. Students complete small projects on topics as diverse as population growth, epidemic transmission, queuing theory, and forest fire outbreaks.

Mathematics Course Offerings for the Data Science Minor

Mathematics course offerings will be found under the 4 letter code of MATH in the course listings.

200 – Introduction to Statistics (3)

First course in statistical methods. Includes descriptive and inferential techniques and probability, with examples from diverse fields. Topics vary with instructor and may also include sampling methods, regression analysis, and computer applications.

300 – Linear Algebra (3)

Prerequisites: MATH 122 and either MATH 201 or CPSC 125. An introduction to linear algebra. Usually includes matrix algebra, systems of equations, vector spaces, inner product spaces, linear transformations, and eigenspaces.