ITI5149 Applied data analysis
Faculty of Information Technology
ITI5149 Applied data analysis is a level 5, 6-credit-point, postgraduate unit from the Faculty of Information Technology. It isn't offered in 2023. It needs ITI5197.
- Credit points
- 6
- Offered in 2023
- Other periods
- Indonesia
- Assessment
- Exam 50%
- and 2 other tasks
- Workload
- 144 hours
- per semester
This is the 2023 handbook entry. See the 2027 entry.
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Requisites
Before ITI5149
Prerequisites
Pass these before you enrol.
Prohibitions
You can't enrol if you have passed any of these.
After ITI5149
No unit lists ITI5149 as a prerequisite in the 2023 handbook.
Enrolment rules
This unit is only available to students enrolled at the Indonesia campus.
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 2023
| Teaching period | Campus | Mode |
|---|---|---|
| Monash Indonesia term 4 | Indonesia | 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
- Tutorials24 hours
- Lectures24 hours
- Teaching approachActive learning
Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per teaching period 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.
Contacts
- Chief Examiners
- Dr Lan Du
Common questions
What are the prerequisites for ITI5149?
You need ITI5197 before you enrol. Enrolment rules also apply.
When is ITI5149 offered?
ITI5149 has no offerings listed in the 2023 handbook.
How much work is ITI5149?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does ITI5149 have an exam?
Yes. The exam is worth 50% of the final mark, alongside 2 other tasks.