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 2020 in Semester 1 and Semester 2 at Caulfield, Clayton and Monash Online. It has no prerequisites and unlocks 1 unit.
- Credit points
- 6
- Offered in 2020
- Semester 1, Semester 2
- Caulfield, Clayton, Monash Online
- Assessment
- Exam 50%
- and 2 other tasks
- Workload
- 144 hours
- per semester
This is the 2020 handbook entry. See the 2027 entry.
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Requisites
Before FIT5149
No prerequisites or corequisites besides the enrolment rules below.
After FIT5149
1 unit list FIT5149 as a prerequisite or corequisite.
Enrolment rules
Prerequisites: FIT5197 or (ETC5252 plus experience with programming in R)
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 2020
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Caulfield | On campus |
| First semester (Fully flex) | Caulfield | Flexible |
| Second semester | Clayton | On campus |
| Teaching period 6 | Monash Online | Mo |
Assessment
- In-semester assessmentThreshold hurdle50%
- Examination (2 hours and 10 minutes)Threshold hurdle50%
- In-semester assessment100%
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.
Contacts
- Chief Examiners
- Dr Lan Du
Common questions
What are the prerequisites for FIT5149?
FIT5149 has no prerequisites, but enrolment rules apply.
What can I take after FIT5149?
FIT5149 is a prerequisite or corequisite for 1 unit, including FIT5213.
When is FIT5149 offered?
In 2020, FIT5149 runs in Semester 1 and Semester 2 at Caulfield, Clayton and Monash Online.
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.