Course Title: Introduction to Analytics

Part A: Course Overview

Course Title: Introduction to Analytics

Credit Points: 12.00

Course Coordinator: Dr. Stelios Georgiou

Course Coordinator Phone: +61 3 9925 3158

Course Coordinator Email:

Pre-requisite Courses and Assumed Knowledge and Capabilities


Course Description

This course will introduce you to fundamental statistical concepts and modern statistical practices. You will study methods for data analysis, including summary statistics, data visualisation and probability as a measure for uncertainty. You will then build upon these topics and learn to perform statistical inference such as hypothesis testing and confidence intervals. There is an emphasis on conceptual understanding, interpretation of statistical output and the use of statistical packages for statistical computation.

Objectives/Learning Outcomes/Capability Development

This course is an elective.

On completion of this course you should be able to:   • Elucidate the concept of variation and identify and pose statistical questions requiring investigation • Plan a statistical data investigation including identifying variables and measures and proposing a method of data collection that will answer the question posed. • Collect, manage and store statistical data ready for analysis. • Apply fundamental statistical methods to explore, analyse and visualise data and test statistical hypotheses • Present, evaluate and analyse statistical data in a useful and clear manner. • Use statistical packages for fundamental statistical analysis of various data. 


Overview of Learning Activities

You will be expected to attend lectures, computer laboratories and tutorial sessions. The lecture, laboratory and tutorial classes will focus on practical problems and applications. 
The instructor will use demonstrations to provide you with an insight into the mental processes one goes through when mapping real life problems to abstract mathematical and statistical procedures and then to pen-and-paper calculations or the use of statistical packages.

Each class will provide opportunities for you to receive feedback while attempting to determine solutions using pen-and-paper and suitable statistical packages. This is critical, as these skills will impact on the other mathematical knowledge and skills developed throughout the program. This will be achieved during lectures, practical sessions in a laboratory style problem solving class, and tutorial classes.

You will spend much of the time working on problems as well as class discussions highlighting the similarities and differences involved in various solution strategies. You would be encouraged to find support from peers and the instructor when appropriate.


Teacher guided hours: 48

Learner directed hours: 72

Overview of Learning Resources

This course will be supported online through myRMIT and will give you access to  important announcements, staff contact details, the teaching schedule, online notes, tests and quizzes, self-help exercises and past exam papers.   WebLearn tests and quizzes.   You are advised to login to myRMIT and read your student e-mail account daily for important announcements. You should also visit Blackboard at least once a day to view important announcements regarding the course and key documentation


Overview of Assessment

☒This course has no hurdle requirements.


Assessment tasks

Early Assessment Task: On line Quiz
Weighting 5%.
This assessment task supports CLOs 1, 2, 3, and 4

Assessment Task 1: Assignments 
Weighting 25%.
This assessment task supports CLOs 1,2 3, 4, 5, and 6

Assessment Task 2:  Computer Labs and on line assignments 
Weighting 20%.
This assessment task supports CLOs 2, 4, 5 and 6

Assessment Task 3: Final Exam 
Weighting 50%.  
This assessment supports CLO 1, 2, 3, and 5