FIT5149 Applied data analysis
Faculty of Information Technology
FIT5149 Applied data analysis is a level 5, 6-credit-point, postgraduate unit from the Faculty of Information Technology, offered in 2022 in Semester 1 and Semester 2 at Clayton. It has no prerequisites.
- Credit points
- 6
- Offered in 2022
- Semester 1, Semester 2
- Clayton
- Assessment
- Exam 50%
- and 2 other tasks
- Workload
- 144 hours
- per semester
This is the 2022 handbook entry. See the 2027 entry.
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Requisites
Before FIT5149
Prohibitions
You can't enrol if you have passed any of these.
After FIT5149
No unit lists FIT5149 as a prerequisite in the 2022 handbook.
Enrolment rules
Prerequisites: FIT5197 or (ETC5252 plus experience with programming in R)
Equivalent units
The same content under another code. Only one of them counts.
Overview
This unit aims to provide students with the necessary analytical and data modeling skills for the roles of a data scientist or business analyst. Students will be introduced to established and contemporary Machine Learning techniques for data analysis and presentation using widely available analysis software. They will look at a number of characteristic problems/data sets and analyse them with appropriate machine learning and statistical algorithms. Those algorithms include regression, classification, clustering and so on. The unit focuses on understanding the analytical problems, machine learning models, and the basic modeling theory. Students will need to interpret the results and the suitability of the algorithms
Offerings in 2022
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Clayton | On campus |
| Second semester | Clayton | On campus |
Assessment
- Assignment 1AssignmentThreshold hurdle15%
- Assignment 2AssignmentThreshold hurdle35%
- Scheduled final assessment (2 hours and 10 minutes)ExamThreshold hurdle50%
Learning outcomes
When you finish this unit, you should be able to:
- 1
Analyse data sets with a range of statistical, graphical and machine-learning tools;
- 2
Evaluate the limitations, appropriateness and benefits of data analytics methods for given tasks;
- 3
Design solutions to real world problems with data analytics techniques;
- 4
Assess the results of an analysis;
- 5
Communicate the results of an analysis for both specific and broad audiences.
Workload and teaching
- Lectures24 hours
- Tutorials24 hours
- Teaching approachActive learning
Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled activities. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning and online engagement.
Learning resources
Required resources
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning
Zhao, Y (2012) R and Data Mining: Examples and Case Studies. Academic Press
Technology resources
Students will need R/Python, which is freely available software that can be download from the Internet.
Where it fits
FIT5149 is part of 1 area of study in the 2022 handbook.
Contacts
- Chief Examiners
- Dr Lan Du
- Unit Coordinators
- Dr Hao Wang
- Dr Lizhen Qu
Common questions
What are the prerequisites for FIT5149?
FIT5149 has no prerequisites, but enrolment rules apply.
When is FIT5149 offered?
In 2022, FIT5149 runs in Semester 1 and Semester 2 at Clayton.
How much work is FIT5149?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does FIT5149 have an exam?
Yes. The exam is worth 50% of the final mark, alongside 2 other tasks.
Which majors and minors include FIT5149?
FIT5149 is part of Computational science.