A Statistician collects and analyzes data to answer questions in healthcare, business, government, science, education, and public policy. You design surveys, experiments, and studies so the information being collected can produce reliable conclusions. You use statistical software and programming tools to clean data, test ideas, measure relationships, and calculate how certain the results are. You create charts, tables, models, and written reports that explain what the numbers mean to researchers, managers, doctors, or government officials. Many Statisticians also work with data scientists, programmers, and subject experts to evaluate medical treatments, predict outcomes, measure public opinion, or improve business decisions. In 2026 and beyond, Statisticians continue using Python, R, SQL, SAS, cloud platforms, and AI-assisted analysis while remaining responsible for the accuracy, fairness, and meaning of the results.
The most common pathway is earning a bachelor’s degree in statistics, mathematics, data science, economics, biostatistics, or a closely related quantitative field, followed by a master’s degree for many professional Statistician positions. Students complete courses in probability, statistical inference, regression, experimental design, survey methods, calculus, linear algebra, databases, and programming while using R, Python, SQL, SAS, SPSS, Excel, Jupyter Notebook, Git, Tableau, or Power BI. Coursework includes cleaning datasets, testing hypotheses, building regression models, calculating confidence intervals, designing surveys, and explaining results in technical reports. Research projects and internships allow students to analyze real medical, business, government, scientific, or survey data while working with researchers and subject experts. Graduate programs commonly add advanced modeling, multivariate analysis, time-series methods, clinical trial design, statistical computing, and independent research using large datasets.
| School | Location | Distance from ZIP Code 61615 |
|---|---|---|
| Western Illinois University | Macomb, Illinois | 59.4 miles |
| University of Illinois Urbana-Champaign | Champaign, Illinois | 86.5 miles |
| Northern Illinois University | DeKalb, Illinois | 91.2 miles |
| University of Iowa | Iowa City, Iowa | 116.4 miles |
| Cornell College | Mount Vernon, Iowa | 122.9 miles |
| University of Chicago | Chicago, Illinois | 125.8 miles |
| University of Illinois Chicago | Chicago, Illinois | 127.4 miles |
| DePaul University | Chicago, Illinois | 128.6 miles |
| Loyola University Chicago | Chicago, Illinois | 131.5 miles |
| Northwestern University | Evanston, Illinois | 134.2 miles |
| Lake Forest College | Lake Forest, Illinois | 135.7 miles |
| Lindenwood University | Saint Charles, Missouri | 141.0 miles |
| Valparaiso University | Valparaiso, Indiana | 141.7 miles |
| Purdue University-Main Campus | West Lafayette, Indiana | 147.5 miles |
| Washington University in St Louis | St. Louis, Missouri | 151.5 miles |
Employers in 2026 look for applicants who can use R, Python, SQL, SAS, or SPSS to clean data, run statistical tests, build regression models, and explain the results in clear reports. A strong internship or research assistantship should include designing a study, checking data quality, documenting analysis code, preparing tables and graphs, and presenting conclusions to researchers or managers. A useful portfolio can include an R Markdown or Jupyter Notebook analysis, a survey design project, a regression model, an experimental analysis, a Tableau or Power BI dashboard, and a GitHub repository containing organized code and documentation. Employers also value direct experience with clinical trials, survey research, forecasting, public-health data, financial modeling, or business analytics because each setting requires specific study designs and reporting standards. A candidate becomes easier to hire when the résumé identifies the size of the datasets analyzed, the statistical methods used, the software applied, and the measurable result, such as improved forecast accuracy, a validated research conclusion, a completed clinical analysis, or a decision supported by reliable evidence.