Statistics major

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 — a Top Five Best Business Job 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, complemented 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!

In addition to the required core courses of the major, you’ll choose an “area of application” for further in-depth study. Starting your sophomore year, you’ll dive into a subject area of interest to you and earn the equivalent of a minor in that discipline. Examples include:

  • Advertising,

  • Bioinformatics,

  • Business administration,

  • Business analytics,

  • Cognitive science,

  • Computational biology,

  • Computer science,

  • Economics,

  • Game studies,

  • International business studies,

  • Management information systems,

  • Organizational and community leadership, and

  • Resource economics.

Will a statistics major prepare me to be a data scientist?

Absolutely. A major in statistics at UD will prepare you for careers or further study in data science, as well as many other fields. Statistics is the basic mathematical science behind data science. It is, in short, the science of data. Statistics refers to every aspect of how we handle and use data such as collecting data; classifying, summarizing, and organizing data; analysis of data using summary measures and graphs; making inferences from a sample to a population; and interpretation of the results. Statistics is both a field of study and a set of tools used by many disciplines, such as business, economics, health sciences, and the social sciences. We provide you the tools to be a data scientist by providing you the technical background in mathematics, statistics, and computer science that you need.  But only in Statistics will you get the depth of knowledge in advanced statistical techniques that makes you a Statistician.

For more information, visit the UD Online Catalog.

Career paths

  • Risk Assessment Analyst
  • Market Researcher
  • Pricing Analyst
  • Federal Statistics Statistician
  • Credit Analyst
  • Sports Statistician
  • Product Development Analyst

Graduate school paths

  • Applied Statistics
  • Data Science
  • Business Analytics
  • Biostatistics
  • Economics
  • Resource Economics
  • Education Statistics

This sample shows just one possible pathway to earning a bachelor of science degree in Statistics in four years. This plan does not replace the advice of your advisor. 

Course highlights

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

Statistics | Undergraduate Programs | University of Delaware
Statistics | Undergraduate Programs | University of Delaware
Statistics | Undergraduate Programs | University of Delaware
Contact us

Noël Hart Wolhar
Associate Director, CANR Undergraduate Recruitment