FIT5197 · Statistical data modelling

Overview

This unit explores the statistical modelling methods that underlie the analytic aspects of Data Science and Machine Learning. By working through examples, this unit gives a strong mathematical and statistical foundation to enable a deeper understanding of data analysis and machine learning methods taught in later MDS/MAI units which focus on machine learning with a more practical perspective. It introduces basic notions about data and foundational mathematics and statistics in the form of sample statistics, probability, expectation and parametrised probability distributions. This provides a basis to introduce statistical inference through maximum likelihood estimation, confidence intervals and hypothesis testing as a way of inferring information about the probability distributions that best describe observed data. Building upon inference models, the unit considers predictive models that predict one data variable based on other data variables through introductory supervised machine learning methods for regression and classification. Unsupervised machine learning methods such as clustering that find hidden groupings in data are also considered.

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.