Data Science

DS 701 Cr.3

Exploratory Data Analysis

This course introduces data science and highlights its importance in decision making. Students learn how to analyze data using the R programming language. During the course, students learn how to import data into R, tidy it, conduct exploratory data analysis, develop visualizations, and draw statistical inferences. The course teaches data wrangling, visualization, and exploration with R. Prerequisite: admission to a graduate Data Science Program. Consent of department. Offered Fall, Spring, Summer.

DS 705 Cr.3

Statistical Methods

Statistical methods and inference procedures are presented with an emphasis on applications, computer implementation, and interpretation of results. Topics include simple, multiple, and logistic regressions; model selection; one-sample, paired-sample, and two-sample t-tests. Prerequisite: DS 701; admission to graduate Data Science Program. Consent of department. Offered Fall, Spring.

DS 710 Cr.3

Programming for Data Science

Introduction to programming languages and packages used in data science. Prerequisite: admission to a graduate Data Science Program. Consent of department. Offered Fall, Spring.

DS 716 Cr.3

Data Management for Data Science

This course explores the various approaches for data management used in data science. Students learn how data is collected, transformed, stored, and delivered for use in data science projects. Prerequisite: admission to a graduate Data Science Program. Consent of department. Offered Fall, Spring, Summer.

DS 730 Cr.3

Big Data: High Performance Computing

This course will teach students how to process large datasets efficiently. Students will be introduced to non-relational databases. Students will learn algorithms that allow for the distributed processing of large datasets across clusters. This course will teach students how to process large datasets efficiently. Prerequisite: DS 710 or concurrent enrollment; admission to MS in Data Science. Consent of department. Offered Fall, Spring.

DS 740 Cr.3

Data Mining and Machine Learning

This course covers data mining and machine learning methods and procedures for diagnostic and predictive analytics. Topics include association rules, clustering algorithms, tools for classification, and ensemble methods. Computer implementation and applications will be emphasized. Prerequisite: DS 705; admission to graduate Data Science Program. Consent of department. Offered Fall, Spring.

DS 750 Cr.3

Data Storytelling

Data storytelling involves using data to tell a compelling narrative that helps audiences understand, engage with, and act on the information. This course combines data analysis with communication techniques to present data in an informative and engaging way. This course is specifically designed as a graduate-level requirement for the MSDS degree, focusing on teaching students how to effectively communicate insights through data storytelling techniques. Participants learn to craft engaging stories that resonate with various audiences and drive decision-making. Prerequisite: DS 701; admission to graduate Data Science Program. Consent of department. Offered Fall, Spring, Summer.

DS 770 Cr.3

Ethical Decision-Making Using Data

This course examines how data science relates to developing strategies for organizations. The emphasis is on using an organization's data assets to inform better decisions. The course investigates the use of data science findings to develop solutions to competitive organizational challenges. Special attention is given to critically examining decisions to ensure that they are ethical and avoid unfair bias. Professional codes of conduct as well as local and international regulations are also considered. Prerequisite: admission to a graduate Data Science Program. Consent of department. Offered Fall, Spring, Summer.

DS 776 Cr.3

Deep Learning

Introduction to the theory and applications of deep learning. The course begins with the study of neural networks and how to train them. Various deep learning architectures are introduced including convolutional neural networks, recurrent neural networks, and transformers. Applications may include image classification, object detection, and natural language processing. Algorithms are implemented in Python using a high-level framework such as Pytorch or TensorFlow. Prerequisite: DS 710, DS 740; admission to MS in Data Science. Offered Fall, Spring.

DS 785 Cr.3

Capstone

This is a capstone course in which students develop and execute a project involving real-world data. Projects include formulation of a question to be answered by the data; collection, cleaning, and processing of data; choosing and applying a suitable model and/or analytic method to the problem; and communicating the results to a non-technical audience. Prerequisite: DS 730, DS 740; admission to graduate Data Science Program. Consent of department. Offered Fall, Spring.