ENG6001 Advanced engineering data analysis
Faculty of Engineering
ENG6001 Advanced engineering data analysis is a level 6, 0-credit-point, postgraduate unit from the Faculty of Engineering, offered in 2023 in Semester 1 at Clayton and Suzhou (SEU). It has no prerequisites.
- Credit points
- 0
- Offered in 2023
- Semester 1
- Clayton, Suzhou (SEU)
- Assessment
- No exam
- 4 tasks
- Workload
- 144 hours
- per semester
This is the 2023 handbook entry. See the 2027 entry.
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Requisites
Before ENG6001
No prerequisites or corequisites besides the enrolment rules below.
After ENG6001
No unit lists ENG6001 as a prerequisite in the 2023 handbook.
Enrolment rules
You must be enrolled in the Engineering PHD program or with permission from the Faculty of Engineering. The unit assumes that you have the necessary foundational knowledge.
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 2023
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Clayton | On campus |
| First semester | Suzhou (SEU) | On campus |
Assessment
- AssignmentsThreshold hurdle30%
- Quiz in problem-solving sessionsThreshold hurdle10%
- Mid-semester assessmentThreshold hurdle20%
- Final assessmentThreshold hurdle40%
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
- Lectures12 hours
- Laboratories20 hours
- Practical activities12 hours
- Assessments3 hours
- Teaching approachProblem-based learning
The minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of 3-6 hours of scheduled learning activities and 6-9 hours of independent study per week. Scheduled activities may include a combination of teacher-directed learning, peer-directed learning and online engagement. Independent study may include associated readings, assessment and preparation for scheduled activities.
Various teaching approaches will be used to deliver the material, including expert-led sessions, problem-solving sessions, and software classes. The electronic version of the material will be available on the Moodle site. In problem-solving sessions, you will work on problem sets under the guidance of an expert. Problems will be available on the Moodle. Solutions to the problems will be also available on the Moodle with an appropriate time gap. You are required to attend the problem-solving sessions, to work on the problem sets and participate in class, answer the online quizzes as a part of your overall assessment. There will be two hours of problem-solving and/or software classes commencing from Week 3. You must enrol in these classes using Allocate+
Contacts
- Unit Coordinators
- Assoc Professor Sudha Mokkapati
- Dr Shuli Luo
- Chief Examiners
- Assoc Professor Sudha Mokkapati
Common questions
What are the prerequisites for ENG6001?
ENG6001 has no prerequisites, but enrolment rules apply.
When is ENG6001 offered?
In 2023, ENG6001 runs in Semester 1 at Clayton and Suzhou (SEU).
How much work is ENG6001?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does ENG6001 have an exam?
No. ENG6001 has 4 assessment tasks and no exam.