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CSC220 Applied Probability & Stats.

Lead Faculty: Dr Ronald Uhlig

Course Description

Introduction to the theory and applications of probability and statistics. Topics include fundamental concepts of probability, conditional probability, random variables, common distributions, and statistical inference (estimation, hypothesis testing, and regression). The emphasis is on developing problem solving skills and applying key results to computing and engineering problems such as algorithm analysis, queuing, and simulation.

Learning Outcomes

  • Identify and use both continuous and discrete probability distributions.
  • Perform hypothesis tests.
  • Understand and be able to use linear regression techniques.
  • Construct estimates and confidence intervals for population parameters.
  • Understand and apply the basic concepts of probability theory and statistics to Computer Science problems.
  • Analyze problems and apply relevant statistical techniques to arrive at a solution.
  • Discuss ethical issues relevant to statistical data collection and reporting.