Data Science (DS)


DS 150  Data Science I: Introduction  3 Credits (3,0)

This course is the first of a two part series geared toward first year data science majors or as general education in data fluency. A survey of introductory material spanning practice in the field will be covered. The course is introductory, and there are no prerequisites. There is an emphasis on setting up and maintaining a computing environment; data formatting, loading, and manipulation; elementary data analysis; and historical and ethical contexts of modern practice. Topics covered may vary by instructor.

DS 151  Data Science II: Foundations  3 Credits (3,0)

This course is the second of a two part series geared toward first year data science majors or as general education in data fluency. A survey of introductory material spanning practice the field will be covered. There is an emphasis on basic programming using an introductory language and basic data analysis and visualization using software packages. Topics covered may vary by instructor.
Prerequisites: DS 150

DS 176  Ethics of Data Science  3 Credits (3,0)

Introduction to ethical considerations in practical data science applications. Recent and current ethical controversies in data science; potential for harm in data collection and analysis; case studies.

DS 244  Data Acquisition and Manipulation  3 Credits (3,0)

Data acquisition and storage. Public data sources; application programming interfaces; design and deployment of web scraping applications; HTML parsing; web browser automation; regular expressions; SQL and NoSQL.
Prerequisites: DS 151

DS 312  Machine Learning  3 Credits (3,0)

Theory and application of machine learning algorithms. Data collection, preparation, and preprocessing; supervised and unsupervised models; data mining; decision trees and random forests; neural networks; bagging methods; generative adversarial networks; support vector machines; lasso regression; ridge regression. Hyperparameter tuning, model validation, and suitability of various algorithms for particular applications.

DS 317  Statistical Software  3 Credits (3,0)

Software for statistical analysis. Script- and GUI-based applications; software-assisted hypothesis testing; application of statistical concepts in a software setting; regression analysis; time series analysis; autocorrelation; ARMA models.
Prerequisites: DS 151 Corequisites: DS 413

DS 411  Data Visualization  3 Credits (3,0)

Principles and practice of effective communication through computer generated data visualizations. Grammar of graphics; commonly used data visualization software packages; interactive data visualizations; data visualizations for exploratory analysis; visualizations to support presentations to technical and nontechnical audiences.
Prerequisites: DS 151 or MA 314.

DS 413  Statistics for Data Science  3 Credits (3,0)

An introduction to statistical techniques for analysis of data. Sampling distributions, central limit theorem, maximum likelihood estimation, sufficient statistics, Bayes estimation, hypothesis testing, likelihood ratio test, Neyman-Pearson lemma, simple linear regression, ANOVA, multiple regression, data exploration, nonparametric methods, statistical software packages.
Prerequisites: MA 412

DS 444  Scientific Visualization  3 Credits (3,0)

Scientific visualization is the representation of data graphically as a means of gaining understanding and insight into the data. This course will introduce different aspects of scientific visualization: computer graphics and related mathematics concepts, application packages for interactive display and analysis of data.
Prerequisites: MA 243 and EGR 115

DS 483  Cloud Computing  3 Credits (3,0)

Computing and data analysis in cloud environments. Challenges and benefits of cloud services; latency, availability, and scalability; configuration of virtualized computing clusters; distributed data storage architectures; distributed data analysis algorithms.

DS 490  Data Science Capstone  3 Credits (3,0)

Capstone course integrating learning outcomes from Data Science program courses applied to projects in the students' areas of concentration. Generating applied questions; determining which data can address the question; locating data; acquiring data; data cleaning; data exploration; modeling and data analysis; data visualization; presentation of results; presenting to technical and nontechnical audiences
Prerequisites: DS Major and Senior Standing