This course serves as an introduction to the uses (and potential misuses) of data in a wide variety of social settings, including the exploration of contemporary techniques to analyze such data. Data acquisition, cleansing, management, analysis, and visualization will be addressed through hands-on projects. Project work will include contemporary social problems addressed using a dynamic set of resources and technologies. An emphasis will be placed on how insights gleaned from data analysis can be used to guide individual and group decision-making scenarios.

This course provides the background for an introductory level, non-trigonometry based calculus course. The topics include a review of the fundamentals of algebra: solutions of linear, fractional, and quadratic equations, functions and their graphs, polynomial, exponential, logarithmic and rational functions, and systems of linear equations.

This course is an introduction to the topics of discrete mathematics, including number systems, sets and logic, relations, combinatorial methods, graph theory, regular sets, vectors, and matrices.

This course introduces statistical methods of extracting meaning from data, and basic inferential statistics. Topics covered include data and data integrity, exploratory data analysis, data visualization, numeric summary measures, the normal distribution, sampling distributions, confidence intervals, and hypothesis testing. The emphasis of the course is on statistical thinking rather than computation. Statistical software is used.

This course is an elementary introduction to the topics of regression and analysis of variance. The statistical software package Minitab will be used to reinforce these techniques. The focus of this course is on business applications. This is a general introductory statistics course and is intended for a broad range of programs.