UnitLevel 3Undergraduate

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 2022 in Semester 1 at Clayton and Malaysia. It needs FIT2086, ETW2111, FIT1006, ETC1010, STA1010, ETC1000, ETW1000, ETF1100 or ETW1010.

Credit points
6
Offered in 2022
Semester 1
Clayton, Malaysia
Assessment
Exam 60%
and 2 other tasks
Workload
144 hours
per semester

This is the 2022 handbook entry. See the 2027 entry.

Reviews

No reviews yet

No reviews yet. Be the first to review FIT3152.

Requisites

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 students 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. Students 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. Students will be encouraged to critically reflect on the data analysis process within their own domain of interest.

Offerings in 2022

Teaching periodCampusMode
First semesterClaytonOn campus
First semesterMalaysiaOn campus

Assessment

  • Assignment 1AssignmentThreshold hurdle
    20%
  • Assignment 2AssignmentThreshold hurdle
    20%
  • Scheduled final assessment (2 hours and 10 minutes)ExamThreshold hurdle
    60%

Learning outcomes

When you finish this unit, you should be able to:

  1. 1

    Demonstrate the ability to transform real world problems into ones that can then be solved using data analytics techniques;

  2. 2

    Cleanse and prepare data for analysis;

  3. 3

    Analyse large data sets using a range of statistical, graphical and machine-learning techniques;

  4. 4

    Validate and critically assess the results of analysis;

  5. 5

    Interpret the results of analysis and communicate these to a broad audience.

Workload and teaching

  • Lectures24 hours
  • Tutorials22 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 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

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 7 areas of study in the 2022 handbook.

Contacts

Unit Coordinators
Dr Prabha Rajagopal
Chief Examiners
Dr John Betts

Common questions

What are the prerequisites for FIT3152?

You need FIT2086, ETW2111, FIT1006, ETC1010, STA1010, ETC1000, ETW1000, ETF1100 or ETW1010 before you enrol.

When is FIT3152 offered?

In 2022, FIT3152 runs in Semester 1 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?

Yes. The exam is worth 60% of the final mark, alongside 2 other tasks.

Which majors and minors include FIT3152?

FIT3152 is part of Advanced computer science, Business information systems, Computational science, Data science and IT for business, and 1 other area of study.

More details

Credit points
6
Level
3
Study level
Undergraduate
Faculty
Faculty of Information Technology
Type
Coursework
EFTSL
0.125
Student contribution
SCA Band 2
Study abroad
Available