Course Title: Mathematical Computing and Algorithms
Part A: Course Overview
Course Title: Mathematical Computing and Algorithms
Credit Points: 12.00
Terms
Course Code |
Campus |
Career |
School |
Learning Mode |
Teaching Period(s) |
MATH2109 |
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 1 2016 |
MATH2109 |
City Campus |
Undergraduate |
171H School of Science |
Face-to-Face |
Sem 1 2017, Sem 1 2018, Sem 1 2019, Sem 1 2020, Sem 1 2021 |
Course Coordinator: Assoc Professor Joseph Apaloo
Course Coordinator Phone: N/A
Course Coordinator Email: joseph.apaloo@rmit.edu.au
Pre-requisite Courses and Assumed Knowledge and Capabilities
None
Course Description
This course is designed to teach you how to conceive and implement a computer program using a mathematical programming language (package) like MATLAB. This is used in particular to construct computer models of mathematical and physical problems. MATLAB, Maple and Mathematica are three of the standard computing packages used by mathematicians. The knowledge and skills developed in this course are usually assumed by an employer. These are often used in conjunction with spreadsheets like Excel to enable the input of data. The skills you gain in this course will also be used in many of the other mathematics and statistics courses in your program. Skills will be developed through individual modelling exercises in the lab following on from appropriate lecture material.
Objectives/Learning Outcomes/Capability Development
This course contributes to the following Program Learning Outcomes for BP083 Bachelor of Science (Applied Mathematics and Statistics):
Knowledge and technical competence
- the ability to use the appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.
Problem-solving
- 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.
On completion of this course you should be able to:
- Write and debug small computer programs
- Model and / or solve a range of mathematical problems using either the in-built functions or by writing your own computer program.
- Interpret and report on the results obtained.
- Interface Matlab with Data Bases and the efficient handling of data from spreadsheets.
Overview of Learning Activities
The course consists of lectorials and computer labs. Recorded l Lectures will be available. Key concepts and their application will be explained and illustrated (with many examples) in lectorials and in online notes and videos. . In computer lab classes, you will build capacity in writing simple programs to solve mathematical problems. This will teach you to think critically and analytically and will allow feedback on your understanding and academic progress. The only way to learn how to programme is to write programs, starting with very small programs and building up the complexity.
Overview of Learning Resources
You have access to the computer laboratory, program documentation, external internet links and access to RMIT Library online and hardcopy resources.
Lab and assignment sheets will be provided as well as access to the same sheets in electronic form on Canvas.
Students may also find the following Library guide useful: http://rmit.libguides.com/mathstats
Overview of Assessment
Assessment tasks
Practical formative hand in assessment
Weighting 10%
This assessment task supports CLOs 1 & 2
Practice based assessments:
Weighting 40%
This assessment task supports CLOs 1 & 2
Authentic-practical evaluations of professional skills:
Weighting 50 %
This assessment task supports CLO 1, 2, 3 & 4