STA501 Scientific Data Analysis (8)

Data is constantly being captured, but variability makes processing this data challenging. Statistics is the science and art of making decisions in the presence of variability. This subject provides a foundation to statistics. It includes an introduction to data science, inferential statistics as well Bayesian statistics. The subject's orientation is towards the sciences and covers both experimental and observational data including big data. The emphasis is on understanding, critically evaluating and then reflecting on a range of vital statistical concepts and applying acquired skills to data interpretation, management and modelling by the use of a modern software package.


Session 1 (30)
Wagga Wagga Campus
Session 2 (60)
Wagga Wagga Campus

Continuing students should consult the SAL for current offering details: STA501. Where differences exist between the Handbook and the SAL, the SAL should be taken as containing the correct subject offering details.

Subject Information

Grading System



One session


School of Computing and Mathematics

Enrolment Restrictions

Available to postgraduate students only.

Students who have completed STA201 or STA401 may not enrol in STA501. This is because a significant  component of material covered in STA501 is similar to material they have already studied in STA201 or STA401.

Incompatible Subjects

STA201, STA401

Learning Outcomes

Upon successful completion of this subject, students should:
  • be able to examine critically and reflect on whether the statistical methodology and conclusions drawn in the media, scientific papers or reports are appropriate;
  • be able to explain data science concepts, classify and synthesise datasets and identify the challenges and benefits of micro and big data;
  • be able to use a statistical package to: explore various types of data, summarise and analyse data appropriately, and present and interpret the output in a clear logical manner;
  • be able to calculate and interpret probabilities, use discrete and continuous random variables and sampling distributions, and assess the suitability of these distributions in probability modelling;
  • be able to synthesise the concepts of statistical inference, correlation and multiple linear regression and apply these to real world problems.
  • be able to evaluate and present results, with an integrated understanding of the underlying theory, in the form of a standard statistical report for specialist and non-specialist audiences; and
  • be able to model data using Bayesian analysis; examine, present and infer the output appropriately; and analyse the differences with standard statistical inference.


This subject will cover the following topics:
  • Introduction to R Commander and Data Science
  • Data Classification and Descriptive Statistics
  • Random Variables and Probability
  • Statistical Inference of Quantitative and Qualitative data for one or more variables
  • Correlation and Multiple Linear Regression
  • Bayesian Analysis

The information contained in the CSU Handbook was accurate at the date of publication: January 2020. The University reserves the right to vary the information at any time without notice.