Introduction to Applied Statistics


Stanford School of Humanities and Sciences


Statistical tools for modern data analysis can be used across a range of industries to help you guide organizational, societal and scientific advances. This course uses industry-standard applications and software (R and Python) for numerical reasoning and predictive data modeling, with an emphasis on conceptual rather than theoretical understanding.



Complete STATS202 or STATS216 with a grade of B+ or better.

Topics include

  • Bootstrap
  • Correlated errors
  • Data snooping
  • Interactions and qualitative variables
  • Multiple linear regression
  • Penalized regression
  • Poisson
  • Regression and prediction
  • Simple linear regression
  • Transformations
  • Variance and cross-validation

Note on Course Availability

The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate certificate homepage.

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