Course Title: Introduction to Analytics

Part A: Course Overview

Course Title: Introduction to Analytics

Credit Points: 12.00


Course Code




Learning Mode

Teaching Period(s)


City Campus


171H School of Science


Sem 2 2019,
Sem 2 2020

Course Coordinator: Dr. Vural Aksakalli

Course Coordinator Phone: +61 3 9925 2277

Course Coordinator Email:

Course Coordinator Location: 8.9.84

Course Coordinator Availability: By appointment and by email

Pre-requisite Courses and Assumed Knowledge and Capabilities


Course Description

This course will introduce you to fundamental concepts in statistics and data analytics. You will study methods for data analysis and modelling, including summary statistics, data visualisation, and probability as a measure of uncertainty. You will then build upon these topics and learn how to perform statistical inference such as hypothesis testing and confidence intervals. You will also learn basic statistical modelling techniques including linear regression and logistic regression. There will be an emphasis on conceptual understanding and the use of Python programming language for statistical computation.

Objectives/Learning Outcomes/Capability Development

Program Learning Outcomes

This course contributes to the following program learning outcomes for BP094 Bachelor of Computer Science:

PLO1: Enabling Knowledge: You will gain skills as you apply knowledge effectively in diverse contexts.

PLO2: Critical Analysis: You will learn to accurately and objectively examine and consider computer science and information technology (IT) topics, evidence, or situations, in particular to: evaluate and compare designs of software artefacts and IT systems on the basis of organisational and user requirements.

PLO3: Problem Solving: Your capability to analyse problems and synthesise suitable solutions will be extended as you learn to: design and implement software solutions that accommodate specified requirements and constraints, based on analysis or modelling or requirements specification. 

On successful completion of this course, you will be able to:

  1. Elucidate the concepts of probability and variation, and pose statistical questions requiring investigation.
  2. Plan a statistical data investigation including identifying variables and measures, and proposing a data collection method that will answer the question posed.
  3. Collect, manage, and store statistical data for further analysis.
  4. Apply statistical methods to explore, analyse, and model data and use these methods for testing statistical hypotheses.
  5. Use Python programming language for visualisation, analysis, and modelling of real-world data. 

Overview of Learning Activities

The course will be delivered through a combination of face-to-face lectures and computer labs. The course will be supported by the Canvas learning management system. We will make heavy use of Canvas, so you need to check your RMIT e-mail regularly for important Canvas announcements. You should also monitor discussion forums on Canvas on a regular basis to benefit from the questions and answers posted in there.

You will undertake 4 hours of learning every week through a combination of lectures and computer labs. It is expected that you will commit a minimum of 3 out-of-class hours per week in independent and collaborative study.

Overview of Learning Resources

A list of prescribed and recommended textbooks for this course will be provided on Canvas. All course materials will be posted on Canvas, including lecture notes, computer lab materials, assessment details, teaching schedule, and staff contact details.

Overview of Assessment

This course has no hurdle requirements.

Assessment Tasks (Weight%):


Assessment Task 1 (20%): Test 1
This assessment task supports CLOs 1, 2, 3, 4, and 5.

Assessment Task 2 (20%):  Test 2

This assessment task supports CLOs 1, 2, 3, 4, and 5.

Assessment Task 3 (15%): Course Project

This assessment task supports CLOs 1, 2, 3, 4, and 5.

Assessment Task 4 (45%): Final Exam
This assessment supports CLO 1, 2, 3, and 4.