Major in statistics at the University of Delaware: youtube.com/watch?v=pWGf1gCipq0
Statistics is the science of collecting, managing, analyzing and interpreting data. Statistics is an essential tool in almost every field in undertaking research, product testing and development, quality control, and decision making. The job market is so hungry for statisticians that it ranks No. 2 in Best Business Jobs according to U.S. News & World Report!
Why major in Statistics?
With the increase in data available to businesses, organizations and consumers, the need to make sense of that data has exploded. As a result, statistics is one of colleges’ fastest growing majors. We use the expression “few are called and fewer are chosen.” Statistics requires excellent math skills, a sense of data structure and manipulation and good problem-solving abilities. The combination of these areas enables statisticians to assist in research and discovery in almost every discipline. To researchers, statistics is a set of tools to help estimate effects and test hypotheses. To statisticians, the field is an exciting combination of theory, method and discovery to guide research and bring products to market faster.
Uniqueness of our program
UD will allow you to apply statistical techniques to real data and real problems, making you highly sought after when it’s time to enter the job market. Our students build a “statistical imagination” in order to address a range of problems in diverse fields. At UD, you will build a firm foundation in statistical theory, complimented by courses in applied statistics and data management using SAS, R, JMP. Our courses apply to problem solving in areas like economics, biology, business or the environment. Couple this major with a minor in data analytics or resource economics, and you’ll land an impactful, high-paying job upon graduation!
For more information, visit the UD Online Catalog.
- Risk Assessment Analyst
- Market Researcher
- Pricing Analyst
- Federal Statistics Statistician
- Credit Analyst
- Sports Statistician
- Product Development Analyst
Graduate school paths
- Data Science
- Business Analytics
- Resource Economics
- Education Statistics
Students use data from a variety of disciplines to explore topics in statistical data analysis, estimation, and inference. Instructors cover graphical displays; measures of position, central tendency, and variability; basic probability rules; discrete probability distributions; binomial distribution; normal and standard normal probability distributions; sampling distributions; the t distribution; confidence intervals and hypothesis tests for one mean or proportion; confidence intervals and hypothesis tests for two means or proportions; correlation and simple linear regression.
On a topic of their choosing, statistics majors complete a research project. The undertaking involves a statistical analysis of real data on a topic chosen and developed by the student, who is responsible for proposing the project; obtaining and collecting data; cleaning and managing the data; doing a statistical analysis; writing a formal paper describing the process and results; and presenting the project.
Instructors focus on calculus-based probability theory as typically applied to statistical analyses. This course is part of a two-semester sequence (STAT470 and STAT471) that serves as the theoretical foundation for statistics majors.
This course covers important applied and theoretical aspects of statistical models to analyze time-to-event data. Basic concepts are introduced, including the hazard function, survival function, right censoring, Kaplan-Meier curves, life table estimator for grouped survival data, kernel smoothing estimator for hazard function, log rank tests and the Cox proportional hazards (PH) models with fixed and time dependent covariates. Instructors will also cover regression diagnostics for survival models, the stratified PH model, and the parametric accelerated failure time regression models.
Students learn statistical theory and learning for network data. Students receive a good background in matrix and statistical learning. Topics include network basics, descriptives, and sampling; latent space, block structure, and random graph model; graph testing, graph embedding, and community detection; and machine learning on graphs.
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