Course Title: Mathematics for Computing 2

Part A: Course Overview

Course Title: Mathematics for Computing 2

Credit Points: 12.00

Flexible Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2412

City Campus

Undergraduate

171H School of Science

Face-to-Face

UGRDFlex21 (All)

Course Coordinator: Dr. Vural Aksakalli

Course Coordinator Phone: +61 3 9925 2277

Course Coordinator Email: vural.aksakalli@rmit.edu.au

Course Coordinator Location: 15.04.03

Course Coordinator Availability: By appointment; by email


Pre-requisite Courses and Assumed Knowledge and Capabilities

None


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 within a Jupyter Notebook environment for analytical computation.


Objectives/Learning Outcomes/Capability Development

Program Learning Outcomes (PLOs):

This course contributes to the following Program Learning Outcomes for BP09421 Bachelor of Computer Science (Studios), BP096P21 Bachelor of Software Engineering (Studios) and BP215 P21 Bachelor of Information Technology (Games and Graphics Programming) (Studios):


PLO1. Enabling Knowledge

You will gain skills as you apply knowledge effectively in diverse contexts.


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.


Course Learning Outcomes (CLOs):

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 as well as 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 lectorials and computer practice sessions. 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.


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:

Assessment Task 1: Weekly Exercises
Weighting 45%
This assessment task supports CLOs 1, 2, 3, 4, and 5.

Assessment Task 2 : Online Final Test
Weighting 20%
This assessment task supports CLOs 1, 2, 3, 4, and 5.

Assessment Task 3: Course Project
Weighting 35%
This assessment supports CLO 1, 2, 3, and 4