Develop the skills required to analyse data captured from engineering enterprises and systems. This course will introduce you to key planning and control mechanisms for effective outcome and performance management, as well as the general principles and methodologies of data analysis and statistics, which form the basis for modelling of engineering systems.

You will work on sample models that will help you to realise the causal relationships of different operating parameters. Furthermore, you'll develop skills in data analysis fundamentals, regression analysis, data mining, forecasting, discriminant analysis, simulation and queuing analysis, project control, decision support management tools—some of which will be developed on spreadsheets.

 

 

Course coordinator

Dr Arun Kumar, Senior Lecturer - School of Engineering

Course objectives

  • Describe, investigate and analyse complex engineering systems and associated issues (using systems thinking and modelling techniques);
  • Develop creative and innovative solutions to engineering problems;
  • Comprehend and apply advanced theory-based understanding of engineering; fundamentals and specialist bodies of knowledge in the selected discipline area to predict the effect of engineering activities;
  • Apply underpinning natural, physical and engineering sciences, mathematics, statistics, computer and information sciences; and
  • Develop creative and innovative solutions to engineering challenges.

Learning outcomes

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

  • Perform a thorough data analysis of the performance data set and summarise the findings;
  • Recognise situations and apply the appropriate forecasting models to represent the trend of the business;
  • Fit some parts of the data set to a regression analysis model and interpret the implication of the model in terms of the enterprise's past and future performance;
  • Define and apply the Monte Carlo technique to a number of different business modelling situations;
  • Restructure the provided data set into different interpretable modelling frameworks;
  • Apply decision-making techniques to the different forms of data models and investigate 'what-if' scenarios;
  • Create innovative solutions to solve problems recognised in the 'what-if' scenarios; and
  • Apply theories of mathematics and statistics to consolidate the provided data to an indicative decision support data structure, then apply decision-making techniques.

Assessment

Assessment for this course will occur at various times across the seven-week teaching period. In most cases, assessment should follow a similar structure to the below:

  • A short assessment may occur in the first couple of weeks, driven mostly by peer-assessment or objective feedback as is the case of a survey quiz or contribution to discussion.
  • Assessments that occur mid-study period (approximately week 2 to 5) will have a highly formative purpose, like an extended case study or a scenario role play. These are intended to provide an indication of performance and occur at this time to enable positive changes to future performance.
  • Final assessments are usually summative, and generally draw the course's threshold concepts together. Your previous assessments will have directly prepared you for a summative-style assessment.

Rich, online feedback will be provided to you throughout the teaching period on practical exercises and by individual consultation, ideally within five business days.


Please note, unit structure and content are subject to change. Contact your RMIT Student Enrolment Advisor on 1300 701 171 for more information based on your particular circumstances.