Data Science Minor

Introduction to computer science. Covers problem solving methods and algorithm development; modern programming methodologies; and fundamentals of high-level block structured language using Python.

Introduction to computer science. Covers problem solving methods and algorithm development, modern programming methodologies, and fundamentals of high-level block structured language using C++.

An introduction to the primary statistical and probabilistic models used in the collection and interpretation of engineering data. The focus is on summary techniques, regression models, and application of the central limit theorem, confidence intervals, hypothesis tests, and analysis of variance.

Mathematical theory and applications, development of formulae, principles of statistical decision theory, descriptive measurements, probability concepts, random variables, normal distribution, inferential statistics, sampling distributions, confidence intervals, hypothesis testing, chi-squared procedures, linear regression, and the use of computers in statistics.

The first semester of a two-semester course providing a systematic development of the theories of probability and statistics. Students learn and use fundamental concepts of probability models, random variables and their distributions, reduction of data, estimation, testing of hypotheses, univariate normal inference, and statistical decision theory. The first semester is required for BA and BS statistics majors of all concentrations.

Overview of current database technologies with an emphasis on relational database technology. Introduction to database design, entity relationship diagraming, structured query language, and stored procedures.

This course is a comprehensive introduction to data management in organizations. It establishes the data management foundation for the computing major. Topics include conceptual and logical data modeling, entity relationship and relational data modeling, and database design and implementation using the SQL programming language.

This course teaches strategies for visual analyses of business data to inform business strategies across a variety of industries. Students will learn how to create and interpret charts, graphs, infographics, interactive dashboards and communicate the visual insights to customers and employers. A variety of visualization tools will be used for practice and preparation for industry certifications, e.g. SAS, Tableau, GIS.

Delve into the essential principles of cutting-edge Geographic Information Systems (GIS) to grasp its theoretical underpinnings and its widespread application across diverse industries. Over the next few decades, the utilization of GIS is expected to experience rapid expansion in fields spanning the arts and sciences, business, engineering, health sciences, nursing, and more. The demand for a foundational skill set in GIS is pressing, and studying GIS and its functionalities aligns perfectly with this demand. Gain insights into GIS terminology and acquire hands-on experience in map design, manipulation of spatial data, and spatial analysis as innovative means to bolster global initiatives.

This course provides the tools to create and critically evaluate data visualizations. Focus will be on statistical graphics, graphics that display statistical data. Additionally, recent advances in the field of information visualization will be covered.

This course covers introductory machine learning topics including supervised and unsupervised learning, linear and logistic regression, support vector machines, neural networks (MLPs, CNNs, RNNs, GANs) and more. Coursework includes instruction and programming assignments in algorithmic implementations and high-level library usage. Students also apply machine learning techniques to a unique research project.

The course commences with an examination of the knowledge discovery process. In particular, the introduction equips students with the strategic thinking skills essential to focus on cutting edge data mining techniques that can be applied in a wide variety of settings, e.g., business, engineering, health care, science, etc. Traditional topics include data mining algorithms and implementation issues, advantages and disadvantages of data mining, and examples of knowledge engineering. Current topics such as ubiquitous, distributed, and spatiotemporal geographic data mining will also be explored. This is a practical hands-on course that culminates in a real-world project implemented via open source tools.

Natural language is ubiquitous, e.g., humans speak and write to communicate, to transfer information, and to document knowledge. Natural Language Processing (NLP) is an integral component in countless information systems requiring advanced manipulation of natural language. In this class, students will be introduced to NLP starting with the concept of understanding words in context and the need for natural language processing in the business environment. The discussion continues with a detailed study of words and is a foundational framework supporting phonetics and speech synthesis. Subsequent topics include concepts of how words are grouped together to form unique grammatical units. The last part of the course, explores solving real-world NLP problems and deals with two key areas: corpus building, feature engineering, and application development. Course material is presented via theory-based lectures, group discussion, and practical labs-a culminating research project will be individually crafted.