FIT3152 Data analytics
Faculty of Information Technology
FIT3152 Data analytics is a level 3, 6-credit-point, undergraduate unit from the Faculty of Information Technology, offered in 2027 in Semester 1 and Semester 2 at Clayton and Malaysia. It needs (FIT1006, ETC1000, ETF1100, FIT2086, ETC1010, ETW1000, ETW1010, ETW2111 or STA1010) and (FIT2094 or FIT3171) and unlocks 2 units, leading on to 41 units in all.
- Credit points
- 6
- Offered in 2027
- Semester 1, Semester 2
- Clayton, Malaysia
- Assessment
- No exam
- 4 tasks
- Workload
- 144 hours
- per semester
Reviews
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Requisites
Before FIT3152
Prerequisites
Pass these before you enrol.
- FIT1006Business information analysisNo reviews yet
- ETC1000Business and economic statisticsNo reviews yet
- ETF1100Business statisticsNo reviews yet
- FIT2086Modelling for data analysisNo reviews yet
- ETC1010Introduction to data analysisNo reviews yet
- ETW1000Business and economic statisticsNo reviews yet
- ETW1010Data modelling and computingNo reviews yet
- ETW2111Business data modellingNo reviews yet
- STA1010Statistical methods for scienceNo reviews yet
Prohibitions
You can't enrol if you have passed any of these.
After FIT3152
2 units list FIT3152 as a prerequisite or corequisite.
Overview
There has been an explosion in the quantity and variety of data collected and routinely analysed by government, business and society at large over recent years. This has been described by some social commentators as the rise of "big data" and and the analysts and practitioners who investigate this data as "data scientists." This unit will introduce you to the analysis of big data and the role of the data scientist. Techniques covered include data management and transformation, visual analysis, social network analysis, statistical learning, clustering and natural language processing. You will be introduced to these methods using open source industry standard software. Data and case studies will be drawn from diverse sources. The general principles of analysis, investigation and reporting will be covered. You will be encouraged to critically reflect on the data analysis process within your own domain of interest.
Offerings in 2027
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Clayton | On campus |
| First semester | Malaysia | On campus |
| Second semester | Clayton | On campus |
Assessment
- Assignment 1Project25%
- Assignment 2Project20%
- Assignment 3Project25%
- Quiz and practical activityQuiz / Test30%
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
Demonstrate the ability to transform real world problems into ones that can then be solved using data analytics techniques;
- 2
Cleanse and prepare data for analysis;
- 3
Analyse large data sets using a range of statistical, graphical and machine-learning techniques;
- 4
Validate and critically assess the results of analysis;
- 5
Interpret the results of analysis and communicate these to a broad audience.
Workload and teaching
- Applied sessions22 hours
- Seminars24 hours
- Teaching approachPeer assisted 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 teaching activities.
Learning resources
Recommended resources
- M. Allerhand. (2011) A Tiny Handbook of R. SpringerLink (Online service), Online access via Library.
- G. James, D. Witten, D, T. Hastie, R. Tibshirani. (2021) An Introduction to Statistical Learning 2nd Ed. Springer. Online access via Library.
- F. Provost and T. Fawcett. (2013) Data Science for Business. O'Reilly Media, Inc. Online access via Library.
- P.-N. Tan, M. Steinbach, V. Kumar. (2006) Introduction to Data Mining. Addison-Wesley.
- W. N. Venables, D. M. Smith. (2021) An Introduction to R. Available from: http://www.cran.rproject.org/.
- H. Wickham, G Gromelund. (2017) R for Data Science. O'Reilly Media, Inc. Also available online from: http://r4ds.had.co.nz/
Technology resources
The R statistical programming language is used for all lecture demonstrations, tutorial exercises and assignments. It is available from https://cran.r-project.org/.R is easiest to use in the RStudio environment. You can download the free version from https://www.rstudio.com/.
Where it fits
FIT3152 is part of 1 area of study in the 2027 handbook.
Contacts
- Unit Coordinators
- Dr Bisan Alsalibi
- Eunus Ali
- Dr John Betts
- Chief Examiners
- Dr John Betts
Common questions
What are the prerequisites for FIT3152?
You need (FIT1006, ETC1000, ETF1100, FIT2086, ETC1010, ETW1000, ETW1010, ETW2111 or STA1010) and (FIT2094 or FIT3171) before you enrol.
What can I take after FIT3152?
FIT3152 is a prerequisite or corequisite for 2 units, including ETW2510 and ETW3510. Those lead on to 41 units in all.
When is FIT3152 offered?
In 2027, FIT3152 runs in Semester 1 and Semester 2 at Clayton and Malaysia.
How much work is FIT3152?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does FIT3152 have an exam?
No. FIT3152 has 4 assessment tasks and no exam.
Which majors and minors include FIT3152?
FIT3152 is part of Software engineering.