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 statisicians that it ranks No. 1 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.
This course engages students in learning and critical thinking about a variety of pressing issues, such as natural resource management, environmental protection, and poverty alleviation in a regional, national, and international context. Instructors integrate natural science, economics, ethics, and policy to improve the wellbeing of people and the environment. Instructors encourage students to think critically about these issues by applying basic policy and economic analysis, considering the ethical dimensions of policy, and assessing indicators of environmental quality and human welfare. Students tackle projects related to sustainability, resilience, and climate change.
This course provides a foundation of retailing management and strategy, which inherently involves exploring consumer behavior. Instructors cover the many facets of the food value chain to broadly understand challenges and opportunities of retailing food products. Additionally, students discuss the factors and behavioral responses that affect food choice. Students learn and apply basic concepts and key terms and complexities and strategies of retail management. Students learn to create a retail strategy and evaluate the dynamics of maintaining a value proposition to remain competitive in the marketplace.
Instructors introduce the economic explanations for new technologies and the way economists view innovation and adoption of new products and techniques. The course moves on to a description of biotechnology and issues from consumer acceptance to government regulations to trade. Students explore risk assessment, intellectual property rights and differences in developing countries. Along the way, other technologies with the potential for significant impacts on agribusiness or beyond are discussed.
Students develop marketing plans, pitching the plan in local and national competitions. The primary objective is to prepare for the student competition at the National Agri-Marketing Association Conference. Students enrolled develop product ideas, implement marketing and advertising techniques, and evaluate financial requirements for an innovative food and agribusiness product or service. Students will work as a team to design a marketing campaign and compose the executive summary of a written marketing plan.
Instructors cover the necessary steps on how to start a food and agribusiness venture. Students write and present a business plan to industry professionals to mimic a real-world experience. The plan incorporates the subject matter learned in previous FABM coursework, so students can apply the knowledge gained in those courses in a practical academic business endeavor.
- 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.
Related student organizations