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 … For more content click the Read More button below. 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.

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.

Offerings

S2-01-CLAYTON-FLEXIBLE

S2-01-MALAYSIA-ON-CAMPUS

Requisites

Prerequisite

Contacts

Chief Examiner(s)

Dr Daniel Schmidt

Unit Coordinator(s)

Dr Bisan Alsalibi

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 summary

This unit has threshold mark hurdles. You must achieve at least 45% of the available marks in the final scheduled assessment, at least 45% in total for in-semester assessments, and an overall unit mark of 50% or more to be able to pass the unit. If you do not achieve the threshold mark, you will receive a fail grade (NH) and a maximum mark of 45 for the unit.

Assessment

1 - Assignment 1

2 - Assignment 2

3 - Assignment 3

4 - Scheduled final assessment

Scheduled and non-scheduled teaching activities

Seminars

Studio activities

Workload requirements

Workload

Learning resources

Technology resources

You are required to regularly check Moodle for announcements regarding the subject.

(For Clayton Campus ONLY) Please note: This is a bring your own device unit. You will be expected to bring a web-connected device (i.e., laptop or tablet) to class to access specialist software. The applications for your class can be accessed at the website move.monash.edu. For more information, visit monash.edu/move

Availability in areas of study

Data science