Probability & Inference

(DSC 381)

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Probability and Simulation Based Inference for Data Science is a statistics-based course necessary for developing core skills in data science and for basic understanding of regression-based modeling. Students can look forward to gaining a foundational knowledge of inference through the simulation process.

What You Will Learn

  • Definition of probabilities and probability calculus
  • Random variables, probability functions and densities
  • Useful inequalities
  • Sampling distributions of statistics and confidence intervals for parameters
  • Hypothesis testing
  • Introduction to estimation theory (Properties of estimators, maximum likelihood esti-mation, exponential families)

Syllabus

  • Events and probability (1 week)
  • Random variables (1 week)
  • Moments and inequalities (1 week)
  • Continuous random variables (1 week)
  • Normal distribution and the central limit theorem (1 week)
  • Sampling distributions of statistics and confidence intervals (1.5 weeks)
  • Hypothesis testing (2 weeks)
  • Introduction to Estimation Theory (1.5 weeks)

Estimated Effort

10 Hours/week

Course Availability

  • Fall 2021
  • Spring 2022

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