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As a statistics major you'll take Core Program courses that lay a foundation for success. Courses in the natural sciences, humanities, and social sciences will help you develop a comprehensive view of statistics. This is not just a theoretical major—you'll be plugged into real problems, real data, and experiential courses. You'll learn how to make data-driven arguments from a Christian perspective.

To learn more, you can also view the program strengths and learning outcomes for this program.

Upon graduation, you will have opportunities to work in a variety of industries, including health care, community development, and data science.

Students looking to get a degree in statistics will take 13 statistics courses, four mathematics courses, a business and technical writing course, and a programming course. Included in this course work is a semester-long research experience that involves a significant use of multivariable statistics in an applied research project.

• Programming I: An introduction to computer programming. Basic notions of abstraction, elementary composition principles, the fundamental data structures, and object-oriented programming technique are introduced. Topics include variables, control structures, arrays, and input/output.
• Business and Technical Writing: Students will study the process, application, and characteristics of business and technical writing, and the way in which writing style, strategies, content, and clarity will relate practically to one’s profession. Concentrates on developing competence in a variety of writing tasks commonly performed in business, law, industry, social work, engineering, agriculture, and medicine. Satisfies Core Program writing-intensive requirement.
• Calculus I: A study of the basic concepts and techniques of calculus for students in all disciplines. Topics include limits, differentiation, integration, and applications. This course is intended for students without any previous calculus credit.
• Calculus II: Continuation of Mathematics 152; a study of transcendental functions, integration techniques, Taylor series approximations, calculus in polar coordinates, vectors, calculus of vector valued functions and applications of calculus. Students with one semester of calculus credit should take this course instead of Mathematics 152.
• Multivariable Calculus: A study of differential and integral calculus of functions of several variables, and line and surface integrals.
• Elementary Linear Algebra: An introductory study of vectors, matrices, linear transformations, vector spaces, determinants, and their applications, with particular emphasis upon solving systems of linear equations.
• Accelerated Introductory Statistics: This course covers the same content and learning objectives as Statistics 131 but in half the time. This course, along with Statistics 202 and Statistics 203, also serves as preparation for Actuarial Exam SRM. Additionally this course, along with Statistics 202, Statistics 203, Statistics 220 and Statistics 352, serves as preparation for Actuarial Exam MAS I. Offered first half of spring semester. Credit will not be given for both Statistics 131 and 132.
• Applied Statistical Models: This course surveys multivariable design and statistical methods used across various disciplines and seen in peer-reviewed research. Topics include multiple and non-linear regression, general linear models, multivariable statistical models, and multifactor experimental design emphasis is on active-learning using group activities and projects, critiquing research, and statistical software. Offered second half of spring semester.
• Generalized Linear Models: This course covers simple linear regression and associated special topics, multiple linear regression, indicator variables, influence diagnostics, assumption analysis, selection of ‘best subset’, nonstandard regression models, logistic regression, and nonlinear regression models. This course, along with Statistics 132 and Statistics 202, also serves as preparation for Actuarial Exam SRM. Additionally this course, along with Statistics 132, Statistics 202, Statistics 220 and Statistics 352, serves as preparation for Actuarial Exam MAS I.
• Experimental Design: Principles, construction and analysis of experimental designs. Completely randomized, randomized complete block, Latin squares, Graeco Latin squares, factorial, and nested designs. Fixed and random effects, expected mean squares, multiple comparisons, and analysis of covariance.
• Complex Data and Hierarchical Models: A course which illustrates statistical modelling techniques for the class of datasets which have correlation between the observations including time series, hierarchical samples, complex survey samples, clusters, family structures, etc. The general linear model is expanded to the general estimating equations approach.
• Statistical Programming in R: Data acquisition, cleaning, and management in R; use of regular expressions; functional and object-oriented programming; graphical, descriptive, and inferential statistical methods; random number generation; Monte Carlo methods including resampling, randomization, and simulation.
• Machine Learning/Modern Data Analysis Methods: An introductory survey of modern machine learning. Machine learning is an active and growing field that would require many courses to cover completely. This course aims at the middle of the theoretical versus practical spectrum. We will learn the concepts behind several machine learning algorithms without going deeply into the mathematics and gain practical experience applying them. We will consider both pattern recognition and artificial intelligence perspectives.
• Introduction to Univariate Probability: An introduction to the theory and techniques of general probability and common univariate probability distributions. Topics include but are not limited to basic set theory, introductory probability rules (independence, combinatorials, conditionals, Bayes theorem, etc.), common univariate distributions (e.g., binomial and normal) and expected value/variance. This course, along with Statistics 216, also serves as preparation for Actuarial Exam P/1. Offered first half of the semester.
• Introduction to Multivariate Probability: An introduction to multivariate probability distributions. Topics include but are not limited to joint probability density functions, conditional and marginal probability distributions, moment generating functions, covariance and correlations, transformations and linear combinations of independent random variables. This course, along with Statistics 215, also serves as preparation for Actuarial Exam P/1. Offered second half of the semester.
• Mathematical Statistics: The theory of hypothesis testing and its applications. Power and uniformly most powerful tests. Categorical data and nonparametric methods. Bayesian vs. Frequentist methods. Other selected topics. This course, along with Statistics 132, Statistics 202, Statistics 203 and Statistics 352, serves as preparation for Actuarial Exam MAS I. Additionally this course, along with Statistics 290 and Statistics 353, serves as preparation for Actuarial Exam MAS II.
• Introduction to Data Science: Introduction to the field of data science and the workflow of a data scientist. Types of data (tabular, textual, sparse, structured, temporal, geospatial), basic data management and manipulation, simple summaries, and visualization. This course also serves as preparation for
  Actuarial Exam PA. Additionally this course, along with Statistics 220 and Statistics 353, serves as preparation for Actuarial Exam MAS II.
• Data Analysis Internship: A semester-long research experience that involves a significant use of multivariable statistics in an applied research project. Students will identify and work with a primary faculty mentor to develop a project proposal prior to enrolling; students will also be supervised by a statistics professor. Part of the course will include an oral and written presentation of results. The course will be offered as needed and is run as an individual study. May be repeated for up to 12 credits. Permission of instructor required.

See the course catalog for more information.

Participate in active research alongside members of the faculty and present your work locally, regionally, and nationally. Our statistics community is nurtured through numerous ways:

• Ladies' lunch: Female faculty and students from the department gather monthly for a ladies’ lunch. The cost of the lunch is funded through grants and the department in an effort to promote women in our STEM fields. The goal of the lunches is to connect female majors and minors to other women in the area for the purpose of supporting, encouraging, and networking.
• Social events: We enjoy spending time together; sometimes this occurs by majors, such as the Actuarial Science Club, Math Club, or statistics gatherings. Other times we have events for math, statistics, and actuarial science together. Activities have included movies, nacho night, pizza parties, and Christmas gatherings.
• Conference attendance: Students have opportunities to attend conferences with professors with some assisting in joint presentations. Conferences are encouraged to provide students experience with professional development opportunities.