ENG6001 - Advanced engineering data analysis - 2018

0 points, SCA Band 2, 0.000 EFTSL

Postgraduate - Unit

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.

Faculty

Engineering

Chief examiner(s)

Professor Manos Varvarigos

Coordinator(s)

Professor Zixiang Xiong

Unit guides

Offered

Clayton

  • First semester 2018 (On-campus)

Malaysia

  • First semester 2018 (On-campus)

Prerequisites

None

Co-requisites

None

Prohibitions

None

Notes

This unit is available only to Engineering PhD students.

Synopsis

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.

Students 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.

Outcomes

On successful completion of this unit students should be able to:

  • demonstrate a sophisticated understanding of concepts in probability, statistical inference and signal processing
  • critically apply data analysis techniques to real engineering problems
  • make sound conclusions from experimental data
  • demonstrate proficiency in use of a computer software for data analysis
  • demonstrate proficiency in presenting with data.

Assessment

Continuous assessment: 50%

Examination: (2 hours) 50%

Students are required to achieve at least 45% in the total continuous assessment component and at least 45% in the final examination component and an overall mark of 50% to achieve a pass grade in the unit.

Workload requirements

3 hours lectures, 2 hours of labs and 7 hours of private study per week.

See also Unit timetable information