Courses & Units

Data Analytics KIT306

Introduction

In today's world, the prevalent use of technology and automation have resulted in an explosion in the quantity of data, often referred to as "big data", accumulated by business and by researchers. Data warehouses have been used to set up repositories for this big data. Data is seen as a critical asset for decision-making. Raw data, however, is of little value. In order to obtain insights from this big data analytical techniques are required to turn the data in the repositories into knowledge, by extracting information and identifying patterns, upon which actions can be taken. This unit will help students appreciate the value of using business intelligence tools, data mining techniques and information visualisation methods for the analysis of big data. In this unit students will explore the concepts and technology of business intelligence and experience designing and building business intelligence systems. Students will also gain an understanding of various methods and techniques and applications for data mining. Students will also investigate information visualization tools and techniques to represent the big data in forms that more readily convey embedded information. Students will gain an understanding of the major research issues in the area of big data.

Summary

Unit name	Data Analytics
Unit code	KIT306
Credit points	12.5
College/School	College of Sciences and Engineering School of Information and Communication Technology
Discipline	Information & Communication Technology
Coordinator	Doctor Wenli Yang
Available as an elective?	Yes
Delivered By	University of Tasmania
Level	Advanced

Availability

Location	Study period	Attendance options		Available to
Hobart	Semester 1	On-Campus		International	Domestic
Launceston	Semester 1	On-Campus		International	Domestic

Key

: On-campus
: Off-Campus
: International students
: Domestic students

Note

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Units are offered in attending mode unless otherwise indicated (that is attendance is required at the campus identified). A unit identified as offered by distance, that is there is no requirement for attendance, is identified with a nominal enrolment campus. A unit offered to both attending students and by distance from the same campus is identified as having both modes of study.

Key Dates

Study Period	Start date	Census date	WW date	End date
Semester 1	26/2/2024	22/3/2024	15/4/2024	2/6/2024

* The Final WW Date is the final date from which you can withdraw from the unit without academic penalty, however you will still incur a financial liability (refer to How do I withdraw from a unit? for more information).

Unit census dates currently displaying for 2024 are indicative and subject to change. Finalised census dates for 2024 will be available from the 1st October 2023. Note census date cutoff is 11.59pm AEST (AEDT during October to March).

About Census Dates

Learning Outcomes

Explain the characteristics of a data set and the objectives of analysing the data set
Apply methodologies and techniques to clean, sample, model, mine and analyse a data set
Evaluate and improve the performance of different machine learning algorithms
Answer a research question after justifying the methodologies and techniques chosen in the process of analysing a data set

Fee Information

Field of Education	Commencing Student Contribution ^1,3	Grandfathered Student Contribution ^1,3	Approved Pathway Course Student Contribution ^2,3	Domestic Full Fee ⁴
029999	$1,118.00	$1,118.00	not applicable	$2,648.00

¹ Please refer to more information on student contribution amounts.
² Please refer to more information on eligibility and Approved Pathway courses.
³ Please refer to more information on eligibility for HECS-HELP.
⁴ Please refer to more information on eligibility for FEE-HELP.

If you have any questions in relation to the fees, please contact UConnect or more information is available on StudyAssist.

Please note: international students should refer to What is an indicative Fee? to get an indicative course cost.

Requisites

Prerequisites

KIT205 OR KIT206 OR KIT214

Teaching

Teaching Pattern	On-Campus enrolments in Hobart and Launceston: Self-Study (on-line): 1hr/week Workshop (on-line): 2hr/week Tutorials (on computer lab): 2 hr/week. For this unit, students are expected to attend on-campus lab classes, an online lab class will be avaiable for special circumstances (permission is required to attend the online tutorial).
Assessment	MyLO Quiz (10%)\|Project Phase 1 (25%)\|Tutorial Tasks (30%)\|Project Phase 2 (35%)
Timetable	View the lecture timetable \| View the full unit timetable

Teaching Pattern

On-Campus enrolments in Hobart and Launceston:

Self-Study (on-line): 1hr/week

Workshop (on-line): 2hr/week
Tutorials (on computer lab): 2 hr/week.

For this unit, students are expected to attend on-campus lab classes, an online lab class will be avaiable for special circumstances (permission is required to attend the online tutorial).

Assessment

MyLO Quiz (10%)|Project Phase 1 (25%)|Tutorial Tasks (30%)|Project Phase 2 (35%)

Timetable

View the lecture timetable | View the full unit timetable

Textbooks

Required	Required readings will be listed in the unit outline prior to the start of classes.
Links	Booktopia textbook finder

The University reserves the right to amend or remove courses and unit availabilities, as appropriate.