Course Title: Numerical Methods/Statistics for Engineers

Part A: Course Overview

Course Title: Numerical Methods/Statistics for Engineers

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2114

City Campus

Undergraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

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

MATH2114

City Campus

Undergraduate

171H School of Science

Face-to-Face

Sem 2 2017,
Sem 2 2018,
Sem 2 2019,
Sem 2 2020,
Sem 2 2021,
Sem 2 2022,
Sem 2 2023

Course Coordinator: Dr Minh Dao

Course Coordinator Phone: +61 3 9925 8483

Course Coordinator Email: minh.dao@rmit.edu.au

Course Coordinator Availability: By appointment, by email


Pre-requisite Courses and Assumed Knowledge and Capabilities

 

Required Prior Study

You should have satisfactorily completed following course/s before you commence this course.

Alternatively, you may be able to demonstrate the required skills and knowledge before you start this course.

Contact your course coordinator if you think you may be eligible for recognition of prior learning.

Assumed Knowledge

To successfully complete this course, you are expected to have capabilities consistent with the completion of VCE Mathematical Methods at Year 12 level. That is, you are expected to be able to correctly perform basic algebraic and arithmetic operations; solve quadratic and other algebraic equations; solve simultaneous linear equations; recognise and apply the concepts of function and inverse of a function; recognise the properties of common elementary functions (e.g. polynomials and trigonometric functions); sketch the common elementary functions; solve mathematical problems involving functions; find the derivative of elementary functions from first principles and combinations of elementary functions using the product, quotient and chain rules; find the anti-derivative (integral) of elementary functions; use integral calculus to determine the area under a curve.


Course Description

Numerical Methods/Statistics for Engineers is a single semester course consisting of two main components: Numerical Methods and Statistics. The course content has been selected, in consultation with the discipline of Chemical and Civil Engineering, to provide the necessary mathematical training that will assist and expand your learning experience.

The topics covered in the numerical methods component include solutions of nonlinear equations, ordinary differential equations and systems of differential equations, simultaneous linear equations, eigenvalues and eigenvectors and linear least-squares.

Topics covered in the statistical component include descriptive statistics, inferential statistics, linear regression and correlation.


Objectives/Learning Outcomes/Capability Development

This course contributes to Program Learning Outcomes (PLOs):

PLO2: Utilise mathematics and engineering fundamentals, software, tools and techniques to design engineering systems for complex engineering challenges. 

 


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

  1. Solve large systems of simultaneous linear equations. Find eigenvalues and eigenvectors of a matrix. Use the least-squares method to obtain a function for data analysis. 
  2. Find solutions of non-linear equations using bisection method, Newton’s methods and secant method and implement using a computer. 
  3. Estimate the solutions of systems of first order ordinary differential equations or higher order ordinary differential equations using various numerical methods and implement using a computer. 
  4. Construct graphical displays of science/engineering data and interpret the role of such displays in data analysis. 
  5. Apply basic statistical inference techniques, including confidence intervals, hypothesis testing and analysis of variance, to science/engineering problems. 
  6. Employ appropriate regression models to determine statistical relationships.


Overview of Learning Activities

You will be actively engaged in a range of learning activities such as lectorials, tutorials, practicals, laboratories, seminars, project work, class discussion, individual and group activities. Delivery may be face to face, online or a mix of both. 

This course is presented using a mixture of recorded lectures, interactive Q&A sessions, and problem-based practicals. An online course site will be used to disseminate course materials, to provide access to online On-line quizzes and online On-line tests for self-assessment and to submit the problem-based computer laboratory assignments.

Key concepts and their applications will be explained and illustrated with many examples. The problem-based weekly practical sessions/computer labs will build your capacity to solve problems and to think analytically and critically. The mathematical and statistical theories and applications will also be reinforced through assessment. The computer laboratory sessions are designed to assist students in applying statistical methods using software packages, such as Microsoft Excel or Minitab for performing the basic statistical analysis of data collected from a broad range of sciences and engineering fields. Assignments will enhance your understanding of the statistical concepts and help you to develop your ability to apply your knowledge to analyse data.

You are encouraged to be proactive and self-directed in your learning, asking questions of your lecturer and/or peers and seeking out information as required, especially from the numerous sources available through the RMIT library, and through links and material specific to this course that is available through myRMIT Studies Course


Overview of Learning Resources

 

RMIT will provide you with resources and tools for learning in this course through myRMIT Studies Course

There are services available to support your learning through the University Library. The Library provides guides on academic referencing and subject specialist help as well as a range of study support services. For further information, please visit the Library page on the RMIT University website and the myRMIT student portal.


Overview of Assessment

Assessment Tasks

Assessment Task 1: Interim discipline-based on-line assessments
Weighting: 14%
This assessment task supports CLOs 1 & 2

Assessment Task 2: Interim discipline-based on-line assessments
Weighting: 32%
This assessment task supports CLOs 2, 3, 4, 5 & 6

Assessment Task 3: Assignments
Weighting: 34%
This assessment task supports CLOs 4, 5 & 6

Assessment Task 4: Discipline based summative assessment
Weighting: 20%
This assessment task supports CLOs: 1, 2 & 3

If you have a long-term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.