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This course covers probability and statistical techniques for data analytics and data science. Topics include: Exploratory data analysis and visualisation. Probability basics, independence, Bayes theorem. Probability models for data, including Binomial, Poisson, Exponential and Normal. Parameter estimation; method of moments and maximum likelihood. Confidence intervals and hypothesis testing: one and two samples, paired samples, proportions. Simple linear and multiple regression. Analysis of Variance. Case studies in R.
Lectures and labs as for ST663.