This is an introductory course in using modern data analysis concepts and tools to gain insight and make decisions in a business or organizational setting. Topics include data storage, business intelligence, basic data mining and modeling, visualization, prediction/forecasting, and clustering/segmentation. Students will complete at least one data analytics project, starting from an original research question and concluding with actionable recommendations.

An introduction to the construction and analysis of least-squares models, including multiple regression, ANOVA, ANCOVA, and mixed models. Generalized linear models will also be presented, with special attention paid to logistic regression and log-linear models. Examples and applications will be drawn from various disciplines, including biology, medicine, economics, engineering, and the social sciences.

Forecasting is the science of predicting future events and outcomes. In this course students will learn how to effectively use both data and theory to create forecasts and how to quantify and communicate uncertainty in forecasts. Topics include random walks, Markov models, time series analysis, Bayesian methods and qualitative forecasting.

Data mining is the study of discovering and assessing patterns, relationships and information within large datasets. This course provides an introduction to data mining with an emphasis on predictive modeling techniques and machine learning algorithms. Examples and applications will be drawn from various disciplines.

Courses covering various topics of interest in this particular discipline are offered regularly. Contact department or program chair for more information.

This course will allow students to apply their knowledge of data wrangling, analysis, and visualization to implement a data project. Students will learn concepts of data and project management, applying their knowledge to the solving of a significant data-rich challenge. PEAK DESCRIPTION: Students will seek out an organization of their choosing (non-profit, commercial business, political campaign, internal to Concordia College group, etc.) and complete a data analytic consulting project. Students will work with 2-4 members to meet with stakeholders of the organization to discuss their data needs, perform data analysis, and propose solutions based on the analysis. Students will also complete a postmortem report documenting their skills gained and how their perspective was changed by the project. Examples of past projects include quantifying the local impact of domestic violence for YWCA, performing an equity analysis for Homeless2Housed, and designing a food preference survey for Emergency Food Pantry.

This course provides an opportunity for individual students to conduct in-depth study of a particular topic under the supervision of a faculty member. Contact the department or program chair for more information.

This course provides an opportunity for individual students to conduct research in a specific area of study, completed under the direction of a faculty mentor. Specific expectations of the research experience to be determined by the faculty. Repeatable for credit. Prerequisite: consent of instructor.