Statistics is the science of learning from data. It has been a rapidly growing science for many years. It is widely used by businesses and government organizations to understand changes in the economy and to make forecasts about future events based on past patterns in their data. The methodology of statistics can be adapted to many types of problems. Due to the extensive development of computers and the collection of large databases, the need for statistical techniques has greatly expanded in recent years. A society like ours, which has become increasingly dependent on its data, has a growing need for statisticians.
The Department of Statistics offers a B.S. degree in statistics. Majors in this program study, collect, and analyze all types of data and report the results of the analysis.
Actuary • Biostatistician • Business Analyst • Census Analyst • Credit Analyst • Customer Analytics Specialist • Data Analyst • Data Manager • Data Scientist • Data Visualization Designer • Educational Researcher • Financial Modeler • Information Systems Analyst • Marketing Analytics Specialist • Quality Control Specialist • R programmer • Research Statistician • Sales Analyst • Statistical Programmer • Sports Analyst • Statistician • Survey Designer • Systems Analyst • Teacher
Visit the Bureau of Labor Statistics to learn more about the outlook for your future career.
Department of Statistics & Biostatistics
University CatalogUnderstand the foundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance. An introduction to statistical programming and statistical inference with hands-on analysis of real-world datasets.
Learn introductory probability using simulation methods. Topics include the estimation and accuracy of probabilities using repeated sampling and simulating conditional probabilities using conditional programming techniques.
Covers random variables, sampling distributions, confidence intervals, and conditional probability. Topics include t-tests, correlation, regression, chi-squared and ANOVA.
Analysis of variance with emphasis on design and analysis of experiments. Uses data from social sciences, science, and business. Other topics include factorial designs, random effects, and nesting.
Studies data visualization and interactive data exploration. Topics include importing, exporting and data merging, graphs and charts, interactive maps, and meaningful visual representations of complex statistics.
Explores concepts and computation with real data applications. Topics include Bayes’ theorem, distributions, and hierarchical models. Computational strategies such as MCMC, model diagnostics and selection are discussed.