FIT5197 - Statistical data modelling

There is a more recent version of this academic item available.

Overview

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.

Offerings

MO-TP2-01-ONLINE-MO

MO-TP5-01-ONLINE-MO

S1-01-CLAYTON-ON-CAMPUS

S1-01-OS-CHI-SEU-ON-CAMPUS

S1-FF-CLAYTON-FLEXIBLE

S2-01-CLAYTON-ON-CAMPUS

Requisites

Prerequisite

Rules

Enrolment Rule

Contacts

Chief Examiner(s)

Dr Levin Kuhlmann

Notes

IMPORTANT NOTICE:
Scheduled teaching activities and/or workload information are subject to change in response to COVID-19, please check your Unit timetable and Unit Moodle site for more details.

Monash Online offerings are only available to students enrolled in the Graduate Diploma in Data Science via Monash Online.

Learning outcomes

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

Perform exploratory data analysis with descriptive statistics on given datasets;

Construct models for inferential statistical analysis;

Produce models for predictive statistical analysis;

Perform fundamental random sampling, simulation and hypothesis testing for required scenarios;

Implement a model for data analysis through programming and scripting;

Interpret results for a variety of models.

Teaching approach

Active learning

Assessment

1 - In-semester assessment

Value %:50

Hurdle type:Threshold

Hurdle description:

This unit contains hurdle requirements which you must achieve to be able to pass the unit. The consequence of not achieving a hurdle requirement is a fail grade (NH) and a maximum mark of 45 for the unit. Monash Online: In-semester assessment: 100%

Offering(s):

First semester, Clayton, Teaching activities are on-campus (ON-CAMPUS)
First semester, Suzhou (SEU), Teaching activities are on-campus (ON-CAMPUS)
First semester (Fully flex), Clayton, Flexible (FLEXIBLE)
Second semester, Clayton, Teaching activities are on-campus (ON-CAMPUS)

2 - Examination (2 hours and 10 minutes)

Value %:50

Hurdle type:Threshold

Hurdle description:

Offering(s):

First semester, Clayton, Teaching activities are on-campus (ON-CAMPUS)
First semester, Suzhou (SEU), Teaching activities are on-campus (ON-CAMPUS)
First semester (Fully flex), Clayton, Flexible (FLEXIBLE)
Second semester, Clayton, Teaching activities are on-campus (ON-CAMPUS)

3 - In-semester assessment

Scheduled and non-scheduled teaching activities

Laboratories

Lectures

Workload requirements

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 activities. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning and online engagement.

Handbook