ENG5001 Advanced data analytics for engineers
Faculty of Engineering
ENG5001 Advanced data analytics for engineers is a level 5, 6-credit-point, postgraduate unit from the Faculty of Engineering, offered in 2027 in Semester 1 at Clayton and Suzhou (SEU). It has no prerequisites.
- Credit points
- 6
- Offered in 2027
- Semester 1
- Clayton, Suzhou (SEU)
- Assessment
- No exam
- 4 tasks
- Workload
- 144 hours
- per semester
Reviews
No reviews yetNo reviews yet. Be the first to review ENG5001.
Requisites
Before ENG5001
No prerequisites or corequisites besides the enrolment rules below.
After ENG5001
No unit lists ENG5001 as a prerequisite in the 2027 handbook.
Enrolment rules
You must be currently enrolled in a master’s course in engineering or related STEM and in a related discipline. The unit assumes that you have the necessary foundational knowledge.
Equivalent units
The same content under another code. Only one of them counts.
Overview
The unit consists of essential components required to develop an advanced data analytics framework in engineering settings. It first sets up the probabilistic foundations for data analysis, including topics such as probability, random variables, expectation, key probability distributions, conditional distributions, hypothesis testing and statistical correlation, then applies these techniques to inspect and assess real-world datasets in an exploratory manner. The second part of the unit covers mainstream machine learning methods (eg neural networks and tree-based models) to perform statistical inference in regression and classification analysis.
The material will be taught in the context of real engineering problems drawn from multiple disciplines. You will be allocated to a group for a semester-long project to build your own data analytics framework, a skill that is increasingly important across all engineering disciplines.
Offerings in 2027
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Clayton | Flexible |
| First semester | Suzhou (SEU) | On campus |
Assessment
- Quiz in workshopsQuiz / Test18%
- Project: Week 4 submissionArtefact25%
- Project: Week 8 submissionArtefact25%
- Project: Week 12 submissionWritten32%
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
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
- Workshops24 hours
- Practical activities24 hours
- Applied sessions12 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 Plus.
Contacts
- Chief Examiners
- Assoc Professor Sudha Mokkapati
- Unit Coordinators
- Assoc Professor Sudha Mokkapati
- Dr Tianqi Gu
Common questions
What are the prerequisites for ENG5001?
ENG5001 has no prerequisites, but enrolment rules apply.
When is ENG5001 offered?
In 2027, ENG5001 runs in Semester 1 at Clayton and Suzhou (SEU).
How much work is ENG5001?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does ENG5001 have an exam?
No. ENG5001 has 4 assessment tasks and no exam.