Modelling for data analysis

What it covers

This unit explores the statistical modelling foundations that underlie the analytic aspects of Data Science. It covers: • Data: collection and sampling, data quality. • Analytic tasks: statistical hypothesis testing, exploratory and confirmatory analysis. • Probability distributions: dependence and independence, multivariate Gaussian, Poisson, Dirichlet, random number generation and simulation of distributions, simulation of samples (bootstrap). • Predictive models: linear and logistic regression, and Bayesian classification. • Estimation: parameter and function estimation, maximum likelihood and minimum cost estimators, Monte Carlo estimators, inverse probabilities and Bayes theorem, bias versus variance and sample size effects, cross validation, estimation of model performance.

Assessment

When and where