This course is intended to provide an introduction into the field of Data Science. Students will develop skills in appropriate technology and basic statistical methods by completing hands-on projects focused on real-world data and address the social consequences of data analysis and application.

Prerequisite(s): STAT 3127 with a minimum grade of C

This course identifies the importance of adequately preparing data for data modeling and predictive analytics. Topics include data retrieval, merging and organization, data cleaning and data visualization.

Prerequisite(s): DSCI 3111 with a minimum grade of C or DATA 3111 with a minimum grade of C

This course investigates the methods for selecting among multiple data models and for evaluating model selection. Topics include logistic regression, model evaluation techniques, cost-benefit analysis using mis-classification costs, graphical evaluation of classification models, association rules and CART models.

Prerequisite(s): DSCI 3112 (may be taken concurrently) with a minimum grade of C

This course investigates characteristics of ethical design of algorithms for predictive models. Topics include opacity, scale and potential damage of data mining algorithms, data accuracy, stereotyping, and proxy variables; data privacy and security.

Prerequisite(s): DSCI 3112 with a minimum grade of C or DATA 3112 with a minimum grade of C

This course provides the student with an opportunity to conduct a full data analytics project approved by a faculty mentor in the student's home department or one recommended by the course instructor.

Prerequisite(s): DSCI 3112 with a minimum grade of C or DATA 3112 with a minimum grade of C

Practical, supervised experience in the field with an approved company or organization. Students will take on projects that require data cleaning, data organization, data modeling, and/or predictive analytics.

Repeatability: Repeatable for credit up to 1 times or 6 hours.