Course Content
Probability theory (events, conditional probability, independence)
Random variables (discrete and continuous)
Expectation and variance
Common distributions (Binomial, Normal, Poisson)
Sampling and Central Limit Theorem
Statistical inference (estimation, confidence intervals)
Hypothesis testing
Regression and correlation
Introduction to data analysis using computational tools (e.g., R and./or Python)
Learning Outcomes
Upon successful completion of the course, students will be able to:
Apply probability theory to computational problems
Analyze discrete and continuous random variables
Identify and use common probability distributions
Apply statistical inference methods
Conduct hypothesis testing and interpret results
Develop and evaluate basic regression models
Analyze datasets using statistical and computational tools
