This unit has been introduced for the online Master of Analytics offered by BusEco. It is based on FIT5197 Statistical data modelling.

On successful completion of this unit, you should be able to:

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.

020399

This unit explores the statistical modelling foundations that underlie the analytic aspects of Data Science. Motivated by case studies and working through examples, this unit covers the mathematical and statistical basis with an emphasis on using the techniques in practice. It introduces data collection, sampling and quality. It considers analytic tasks such as statistical hypothesis testing and exploratory versus confirmatory analysis. It presents basic probability distributions, random number generation and simulation as well as estimation methods and effects such as maximum likelihood estimators, Monte Carlo estimators, Bayes theorem, bias versus variance and cross validation. Basic information theory and dependence models such as regression and log-linear models are also presented, as well as the role of general modelling such as inference and decision making, and predictive models.

Monash online

In-semester assessment: 100%

1. Assignment 1 - Basic data modelling - 20% - ULOs 1, 4
2. Assignment 2 - Exploratory data analysis, statistical inference, modelling and prediction - 30% - ULOs 3, 4, 5, 6
3. Final quiz - 50% - ULOs 1, 2, 3, 4, 6

Contact hours for Monash Online students:

• Two hours/week online group sessions.

Online students generally do not attend lecture, tutorial and laboratory sessions, however should plan to spend 22 hours per week working through resources and participating in discussions.

FIT

MAT9004 and ITO4133