STAT30280 Inference for Data Analytics (online)

Academic Year 2021/2022

Purposes of statistical inference and learning from data. What is learnt, how it is done and why. Theory covering probability, random variables, likelihoods. Algorithms for performing inference, including implementation using software. Comparison of methods and how to critically assess them.

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Curricular information is subject to change

Learning Outcomes:

By the end of the course, students should be able to:
- identify which distribution is appropriate for certain types of data
- understand the purposes of inference and how to do it in practice
- estimate parameters and their associated uncertainty via likelihood methods, and interpret these values in the context of real-world problems.
- incorporate prior information into common statistical problems and obtain posterior probability distributions of parameters of interest.

Indicative Module Content:

Background of what is inference, recap on probability theory, random variables, common distributions for data.
Random vectors, independence, and conditional distributions.
Expectation, covariance, correlation.
Properties of random samples and asymptotics.
Frequentist statistical inference (method of moments, MLE, confidence intervals).
Uncertainty of estimates: parametric and non-parametric.
Numerical inferential algorithms.
Hypothesis testing.
Introduction to Bayesian inference.
Decision theory.

Student Effort Hours: 
Student Effort Type Hours
Specified Learning Activities


Autonomous Student Learning


Online Learning




Approaches to Teaching and Learning:
Weekly video lectures, ungraded practice problem sheets, ungraded computer labs, graded assignments. 
Requirements, Exclusions and Recommendations
Learning Requirements:

Calculus: familiarity with differentiation and integration. Students are required to have completed an introductory statistics module such as STAT10060 or STAT40720.

Module Requisites and Incompatibles
STAT20100 - Inferential Statistics

Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Examination: 2 hour end of trimester online written exam 2 hour End of Trimester Exam Yes Other No


Continuous Assessment: Tutorial sheets and computer lab exercises Throughout the Trimester n/a Other No


Carry forward of passed components
Resit In Terminal Exam
Summer Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

1. Computer Age Statistical Inference - Algorithms, Evidence, and Data Science", by Bradley Efron and Trevor Hastie.
2. All of Statistics - A Concise Course in Statistical Inference", by Larry Wasserman.
Name Role
Mr John O'Sullivan Tutor