Mathematics

Students integrate mathematics and critical thinking to explore real world topics. The course covers aspects of personal finance, statistical studies, and mathematical modeling. Individual topics include budgeting, saving and borrowing money, measures of center and spread, normal distributions, correlations, and linear, exponential, logarithmic, and trigonometric functions. Spreadsheets are used throughout the course.

This course provides an overview of the field of statistics including gathering and analyzing numerical information. Students study the concepts of sampling, experimentation and measurement and examine statistical methods to study them. Students study confidence intervals and hypothesis testing, including t-tests, F-tests, Chi-Square tests, regressions and analysis of variance. The use of statistical software and/or graphing calculators is included.

This course covers the theory and applications of descriptive and inferential statistics. Statistical methods are implemented using the R statistical programming language. No prior experience with R is required. Specific topics include study design and sampling, describing data distributions, the normal distribution, confidence intervals, hypothesis testing, chi-square distributions, correlations, and ANOVA.

Linear algebra is a branch of mathematics dedicated to solving systems of linear equations using matrices. There are broad applications of linear algebra in the natural and social sciences, business, economics, and engineering, wherein problems routinely involve multiple variables. In this course, we consider real world linear algebra problems and learn how to solve them using the tools of Python programming. Topics include matrix operations, linear dependence and independence, determinants, eigenvectors and eigenvalues, symmetry, and transformations. No prior programming experience is required.

Calculus is the analytical study of functions, including their rates of change (derivatives) and their accumulation (integrals). In this course, we learn the basic theory and techniques of calculus, and study authentic applications to real-world problems. Topics include mathematical modelling with functions, limits, differentiation, optimization, anti-differentiation, and techniques of integration.

Methods used to gather and analyze big volumes of data are collectively called data analytics. The use of computers in our social, economic, and health related activities results in creation of large volumes of data. The analysis of the data can help organizations to discover patterns and better understand processes. This course aims at improving students’ ability to extract information from large volumes of data related to business, health sciences, and social sciences. The students will learn how to describe and analyze large real-world data sets, identify trends and patterns, and draw conclusions by using relevant software and computer programs. Integrative Experience Course