The minor in Data Science 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 Science 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 Science is designed to provide students with the core fundamental coursework in mathematics, computer science, and business to succeed in this area.
Requirements for Data Science Minor
Fifteen (15) credits to include MATH 200; CPSC/DSCI 219, and any three (3) of the following: DSCI 401, 402, CPSC 419, 420, MATH 300, or any BUAD, DSCI, CPSC, or MATH course numbered 300 or higher, approved by the program director.
Decision Sciences (DSCI) Course Offerings for the Data Science Minor
219 – Foundations for Data Science (3)
Skills and tools in acquiring, parsing, manipulating, and preparing data for statistical analysis. Course previously taught as BUAD 219. Cross listed as CPSC 219.
401 –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. Course previously taught as BUAD 403.
402 – 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. Course previously taught as BUAD 400.
Computer Science (CPSC) Course Offerings for the Data Science Minor
220 – Computer Programming and Problem Solving (4)
Prerequisite: CPSC 109 or 110 or 219 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.
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 (MATH) Course Offerings for the Data Science Minor
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.