Learning Outcomes

Program Goals and Student Learning Outcomes

Goal #1 Statistical

Students are trained and experienced in statistical reasoning, designing studies (including practical aspects), exploratory analysis of data by graphical and other means, and a variety of formal inference procedures.

  • Knowledge of statistical theory (e.g., distributions of random variables, point and interval estimation, hypothesis testing, Bayesian methods)
  • Knowledge of graphical data analysis methods
  • Competency in the design of studies (e.g., random assignments, replication, blocking, analysis of variance, fixed and random effects, diagnostics in experiments, random sampling, stratification in sample surveys, data exploration in observational studies)
  • Be able to do statistical modeling (e.g., simple, multiple, and logistic regression; categorical data; diagnostics; data mining)

Goal #2 Computational

Students become familiar with standard statistical software, data management, and algorithmic problem solving.

  • Have an understanding of programming concepts and their applications in statistics
  • Knowledge of the professional statistical software appropriate for a variety of tasks

Goal #3 Mathematical

Students gain knowledge of probability, statistical theory, and any prerequisite mathematics (especially calculus and linear algebra).

  • Knowledge of calculus (integration and differentiation) through multivariable calculus
  • Knowledge of linear algebra (emphasis on matrix manipulations, linear transformations, projections in Euclidean space, eigenvalue/eigenvector decomposition, and singular value decomposition)
  • Probability; emphasis on connections between concepts and their applications in statistics

Goal #4 Communicating and Consulting

Students are able to write clearly and speak fluently, and have developed skills in collaboration and teamwork, as well as organizing and managing projects.

  • Be able to demonstrate effective technical writing and presentations skills
  • Demonstrate teamwork and collaborative skills
  • Demonstrate effective planning for data collection
  • Competency in data management