Course Title: Introduction to Statistical Computing

Part A: Course Overview

Course Title: Introduction to Statistical Computing

Credit Points: 12.00


Course Code




Learning Mode

Teaching Period(s)


City Campus


145H Mathematical & Geospatial Sciences


Sem 1 2006,
Sem 1 2007,
Sem 1 2009,
Sem 1 2013,
Sem 1 2014,
Sem 1 2015,
Sem 2 2015,
Sem 1 2016,
Sem 2 2016


City Campus


171H School of Science


Sem 1 2017,
Sem 2 2017,
Sem 2 2018,
Sem 1 2019,
Sem 2 2019,
Sem 1 2020,
Sem 2 2020

Course Coordinator: Dr. Alice Johnstone

Course Coordinator Phone: +61 3 9925 2683

Course Coordinator Email:

Course Coordinator Location: 015.03.012

Course Coordinator Availability: by appointment

Pre-requisite Courses and Assumed Knowledge and Capabilities


Course Description

Introduction to Statistical Computing is ordinarily taken in the first postgraduate year in preparation for software usage demands in the workplace. Extensive use will be made of Microsoft Excel and SAS code (including SQL syntax) to explore applied statistical problems.

Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes for MC004 Master of Statistics and Operations Research and MC242 Master of Analytics:


Personal and professional awareness

  • the ability to contextualise outputs where data are drawn from diverse and evolving social, political and cultural dimensions
  • the ability to reflect on experience and improve your own future practice
  • the ability to apply the principles of lifelong learning to any new challenge.

Knowledge and technical competence

  • an understanding of appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.


  • the ability to bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
  • an understanding of the balance between the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution.


  • the ability to effectively communicate both technical and non-technical material in a range of forms (written, electronic, graphic, oral), and to tailor the style and means of communication to different audiences.  Of particular interest is the ability to explain technical material, without unnecessary jargon, to lay persons such as the general public or line managers.

Information literacy

  • the ability to locate and use data and information and evaluate its quality with respect to its authority and relevance.

On completion of this course you should be able to:


  1. Construct and interpret visual presentations of data;
  2. Summarise data trends and anomalies using statistical software;
  3. Apply and interpret computational procedures for inference from univariate and bivariate data;
  4. Clearly and concisely communicate data analyses results in the format of a scientific report.

Overview of Learning Activities

The course is designed to help students explore data using different packages that they can use for data analysis tasks they will carry out in other courses in the program and in their working life. They will be able to transfer data easily between applications. They will also be able to communicate information from the data to colleagues in simple but meaningful ways.

Total study hours

You are expected to attend 3 hours of class each week. It is also recommended that on average, 4-6 hours each week are dedicated for independent study including course review and assessment completion.

Overview of Learning Resources

A list of recommended textbooks for this course is provided on Canvas.

All course materials, including lecture notes, lab exercises, practical exercises and assignments will be posted on Canvas LMS.

The statistical package SAS Enterprise Guide can be accessed from the School computer labs, as well as through the RMIT MyDesktop system anywhere and  anytime.  

Library Subject Guide for Mathematics & Statistics


Overview of Assessment

 This course has no hurdle requirements.

Assessment Tasks

Assessment Task 1: Assignments
Weighting 35%
This assessment task supports CLOs 1, 2, 3 and 4.

Assessment Task 2: Midsemester Test
Weighting 25%
This assessment task supports CLOs 1, 2 and 3.

Assessment Task 3: End of Semester Test
Weighting 40%
This assessment task supports CLOs 1, 2 and 3.