Real-world data are commonly messy, distributed, and heterogeneous. This course introduces core concepts of data cleaning and standardisation, and data integration, that are aimed at converting and mapping raw data into other formats that allow more efficient and convenient use and analysis of data. The courses also discusses data quality, management, and storage issues as relevant to data analytics.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
- Critically reflect upon different data sources, types, formats, and structures,
- Research, justify and apply data cleaning, preprocessing, and standardisation for data analytics,
- Apply data integration concepts and techniques to heterogeneous and distributed data,
- Interpret, assess and discuss data quality measurements,
- Research and justify advanced data wrangling, data integration, and database techniques as relevant to data analytics
Indicative Assessment
- Written and practical assignments (60) [LO 1,2,3,4,5]
- Final examination (40) [LO 1,2,3,4,5]
The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.
Workload
The workload for the course is around 130 hours, including reading, the viewing of online course material, participation in face-to-face lectures, practical labs and tutorials, and preparation for assessments.
Inherent Requirements
None
Requisite and Incompatibility
Prescribed Texts
None
Preliminary Reading
- Data matching - Concepts and techniques for record linkage, entity resolution and duplicate detection (Peter Christen, Springer, 2012). This book is a required text for major parts of the course. There are several copies available in the ANU library.
- Data mining: Concepts and techniques, 3rd edition (Jiawei Han, Micheline Kamber and Jian Pei, Morgan Kaufmann, 2011) Note: This is also the text book for the data mining course (COM3425 and COMPP8410).
- Data mining with Rattle and R is a useful book if you plan to use Rattle in this course as well as the Data Mining course (COMP3425 and COMP8410).
Fees
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- 2
- Unit value:
- 6 units
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2027 | $5640 |
- International fee paying students
| Year | Fee |
|---|---|
| 2027 | $7200 |
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
Second Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 10380 | 26 Jul 2027 | 02 Aug 2027 | 31 Aug 2027 | 29 Oct 2027 | In Person | N/A |