FIT2086 · Modelling for data analysis

Overview

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.

Areas of study: Data science

Offerings

Assessment

The Handbook lists an examination for this unit.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. Perform exploratory data analysis with descriptive statistics on given datasets;
2. Construct models for inferential statistical analysis;
3. Produce models for predictive statistical analysis;
4. Perform fundamental random sampling, simulation and hypothesis testing for required scenarios;
5. Implement a model for data analysis through programming and scripting;
6. Interpret results for a variety of models.

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled teaching activities.

Student experience, not official rules. Nothing here changes what the Handbook says.