MonMap

FIT20866cpHandbook

Level 2S2

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.

all of

any of

MTH1035Techniques for modelling (advanced)

MTH1030Techniques for modelling

MAT1841Continuous mathematics for computer science

FIT1058Foundations of computing

ENG1005Engineering mathematics

any of

FIT1045Introduction to programming

FIT1053Introduction to programming: Advanced

Modelling for data analysis

FIT2086

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.