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 |
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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 | ||
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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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Key Dates
Study Period | Start date | Census date | WW date | End date |
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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).
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
The 2024 Commonwealth Supported Place (CSP) rates are still being finalised by the Government and we will update the domestic fee information as soon as we have more details.
Requisites
Prerequisites
KIT205 OR KIT206 OR KIT214Teaching
Teaching Pattern | On-Campus enrolments in Hobart and Launceston: Self-Study (on-line): 1hr/week Workshop (on-line): 2hr/week
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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 |
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