The mission of the discipline is to create and apply statistical methods for collecting, storing, exploring, analyzing, processing and communicating qualitative/quantitative information and to disseminate this knowledge through teaching, scholarly activity, collaboration and outreach. Statistics is the science and art of enhancing knowledge in the face of uncertainty. In our information age, statistics and data science are central to solving problems in the environment, medicine, law, industry, technology, finance, business, public policy, computing, and science in general. The need for statistics applies to almost every area of our lives. The statistics program provides an operational knowledge of the theory and methods of statistics and the application of statistical methods in a liberal arts environment. It seeks to enhance students' critical thinking in making judgments based on data and provides students with the basic knowledge and skills to make contributions to modern society. Students learn to communicate and collaborate effectively with people in other fields and understand the substance of these fields. The curriculum prepares students to enter graduate school or pursue careers in statistics and data science. The statistics discipline has the following student learning objectives: • Students will gain the ability to make contributions to society through knowledge of statistical theory and statistics applied to other disciplines. • Students will sharpen their ability to extract useful information from data. • The statistics curriculum will enhance students’ understanding of the mathematical foundations of statistical theory and methods. • The curriculum will prepare students to enter graduate school, and pursue careers in applied statistics. • Students will be able to communicate statistical ideas and results effectively using presentation skills and visualizations. The curriculum is designed to ensure that students are able to demonstrate the following outcomes: • Model and solve real-world problems by analyzing them statistically, and determine an appropriate approach towards its solution. • Write, read, and construct proofs of key statistical results. • Create estimated models, data displays, and new datasets to address problems using computing tools. • Demonstrate basic knowledge of calculus, analysis, linear algebra, probability, and describe their importance to statistics. • Demonstrate students have background to be employed or gain admission to graduate school. • Meet the requirements for employment in professions such as actuarial science and data science. • Describe and explain a theorem, statistical model, and results of a statistical analysis to a non-specialist audience.

The GPA in these courses must be at least 2.00. Courses may not be taken S/N, unless offered S/N only. Recommended electives for students planning to pursue graduate work in statistics or biostatistics: • MATH 2101 - Calculus III • MATH 6111 - Linear Algebra Recommended electives (beyond those listed for graduate work) for students planning to pursue a PhD in statistics or biostatistics: • MATH 2202 - Mathematical Perspectives • MATH 3221 - Real Analysis I