Course Title: Scientific Computing

Part A: Course Overview

Course Title: Scientific Computing

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH1155

City Campus

Undergraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

 Sem 1 2006,
Sem 1 2007,
Sem 1 2008,
Sem 1 2009,
Sem 1 2010,
Sem 1 2011,
Sem 1 2012,
Sem 1 2013,
Sem 1 2014,
Sem 1 2015,
Sem 2 2016

MATH1155

City Campus

Undergraduate

171H School of Science

Face-to-Face

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

Course Coordinator: Dr Ian Grundy

Course Coordinator Phone: +61 3 9925 3220

Course Coordinator Email: ian.grundy@rmit.edu.au

Course Coordinator Location: 8.9.27

Course Coordinator Availability: please e-mail for an appointment


Pre-requisite Courses and Assumed Knowledge and Capabilities

It will be assumed that you know the basics of programming through successful completion of the courses MATH2109 Mathematical Computing and MATH2313 Problem Solving and Algorithms. It will also be assumed that you have the basic mathematical skills that are developed in MATH1142 Calculus and Analysis 1 and MATH1144 Calculus and Analysis 2


Course Description

Scientific Computing builds on the MATLAB programming skills learnt in Mathematical Computing (MATH 2109) by introducing you to a selection of other commonly used programming environments such as C, Maple, R, Python or Julia.
You will learn the commands and structures of the selected languages and how they are applied in a scientific context. To develop and test your scientific programming skills, you will undertake a mixture of in-class computer laboratory work and challenging out-of-class programming assignments.
 


Objectives/Learning Outcomes/Capability Development

On completion of this course you should be able to:

  1. Analyse a range of mathematical problems, model and / or solve them using an appropriate method and implement the solutions using one or more of the commonly-used programming environments listed above.
  2. Document your computer code so that others can understand it more easily
  3. Interpret and report on the results obtained.


This course contributes to the following Program Learning Outcomes for BP083 - Bachelor of Science (Mathematics) and BP245 - Bachelor of Science (Statistics):

Knowledge and technical competence

  • use appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools. 

Problem-solving

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


Overview of Learning Activities

You will attend lectures where theory will be progressively presented. In computer laboratory sessions individual skills components will be practised and you can seek one-on-one assistance. In addition, problem-based assignments will be given to facilitate the integration of component skills.
The assessment for this course includes the completion of weekly laboratory work, two assignments and two closed book tests. Feedback on your laboratory and assignment work will be provided to you during the semester.

You will undertake the equivalent of four hours per week in lectures and labs. In addition you can expect to spend a minimum of six hours per week in independent study.
 


Overview of Learning Resources

You will have access to the course site on Canvas, computer laboratory, program documentation, external internet links and access to RMIT Library online and hardcopy resources. 
http://rmit.libguides.com/mathstats
 


Overview of Assessment

Note that:
 This course has no hurdle requirements.

Early Assessment Task :  Laboratories 1 - 4
Weighting 10%
Note: The labs are completed and returned weekly.
This assessment task supports CLOs 1, 2 & 3

Assessment Task 2:  Laboratories 5 - 12
Weighting 20%
Note: The labs are completed and returned weekly.
This assessment task supports CLOs 1, 2 & 3

Assessment Task 3:  Assignment 1
Weighting 10%
Note: Distributed in Week 4
This assessment task supports CLOs 1, 2 & 3

Assessment Task 3:  Test 1
Weighting 25%
Note: Closed book under exam conditions, Week 6
This assessment task supports CLOs 1, 2 & 3

Assessment Task 5:  Assignment 2
Weighting 10%
Note: Distributed in Week 10
This assessment task supports CLOs 1, 2 & 3

Assessment Task 6:  Test 2
Weighting 25%
Note: Closed book under exam conditions, Week 12
This assessment task supports CLOs 1, 2 & 3