ENG5001 Advanced engineering data analysis
Faculty of Engineering
ENG5001 Advanced engineering data analysis is a level 5, 6-credit-point, postgraduate unit from the Faculty of Engineering, offered in 2020 in Semester 1 at Clayton, Malaysia and Suzhou (SEU). It has no prerequisites.
- Credit points
- 6
- Offered in 2020
- Semester 1
- Clayton, Malaysia, Suzhou (SEU)
This is the 2020 handbook entry. See the 2027 entry.
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Overview
The unit consists of a review of probabilistic foundations for data analysis including probability, random variables, expectation, distribution functions, important probability distributions, central limit theorem, random vectors, conditional distributions and random processes.
You will develop the foundations of statistical inference including estimation, confidence intervals, maximum likelihood, hypothesis testing, least-squares and regression analysis.
A selection of more advanced topics in probability, random modelling and statistical inference will also be presented.
The material will be taught in the context of real engineering problems taken from multiple engineering disciplines. A widely used numerical computing environment will be used extensively throughout the unit.
Offerings in 2020
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Clayton | On campus |
| First semester | Malaysia | On campus |
| First semester | Suzhou (SEU) | On campus |
| First semester (Fully flex) | Clayton | Flexible |
Learning outcomes
When you finish this unit, you should be able to:
- 1
Assess problems from an engineering perspective and deliberate on the relevant contextual factors. Combine and apply sophisticated data analysis methods and decision-making skills to analyse industrial scenarios and make recommendations that support business growth and development.
- 2
Justify the use of appropriate computer modelling techniques and experimental methods, whilst ensuring model or test applicability, accuracy and limitations of the methods.
- 3
Collaboratively evaluate an industry scenario to solve a problem or develop an innovation.
- 4
Demonstrate the effective communication of the outcomes in a written and verbal format and assess the work of others.
Workload and teaching
3 hours of workshops, 2 hours practical class and 7 hours of private study per week, including online work.
Contacts
- Unit Coordinators
- Dr Xu Yang
- Dr Mehrtash Tafazzoli Harandi
- Associate Professor Hung Yew Mun
- Chief Examiners
- Professor Tom Drummond
Common questions
What are the prerequisites for ENG5001?
ENG5001 has no prerequisites.
When is ENG5001 offered?
In 2020, ENG5001 runs in Semester 1 at Clayton, Malaysia and Suzhou (SEU).