Data Analytics and Business Intelligence (8696.4)
|Available teaching periods||Delivery mode||Location|
|View teaching periods|| On-Campus
|| UC - Canberra, Bruce
|0.125||3||Faculty Of Science And Technology|
|Discipline||Study level||HECS Bands|
|Academic Program Area - Technology||Level 3 - Undergraduate Advanced Unit|| Band 1 2021 (Commenced After 1 Jan 2021)
Band 1 2021 (Commenced Before 1 Jan 2021)
Band 2 2013-2020 (Expires 31 Dec 2020)
Learning outcomesOn successful completion of this unit, students will be able to:
1. Source and access data from a variety of databases;
2. Select and apply appropriate tools for data visualization;
3. Select and apply descriptive data analytics methods;
4. Select and apply predictive data analytics methods;
5. Fit statistical models; and
6. Use the results to produce business intelligence in a variety of settings.
Graduate attributes1. UC graduates are professional - communicate effectively
1. UC graduates are professional - display initiative and drive, and use their organisation skills to plan and manage their workload
1. UC graduates are professional - employ up-to-date and relevant knowledge and skills
1. UC graduates are professional - take pride in their professional and personal integrity
1. UC graduates are professional - use creativity, critical thinking, analysis and research skills to solve theoretical and real-world problems
1. UC graduates are professional - work collaboratively as part of a team, negotiate, and resolve conflict
2. UC graduates are global citizens - behave ethically and sustainably in their professional and personal lives
2. UC graduates are global citizens - make creative use of technology in their learning and professional lives
3. UC graduates are lifelong learners - adapt to complexity, ambiguity and change by being flexible and keen to engage with new ideas
|Year||Location||Teaching period||Teaching start date||Delivery mode||Unit convener|
|2021||UC - Canberra, Bruce||Semester 2||02 August 2021||On-Campus||Dr Yibe Alem|
|2022||UC - Canberra, Bruce||Semester 2||01 August 2022||On-Campus||Dr Yibe Alem|
Tan, P. N. (2019). Introduction to data mining, second edition (required - available for purchase at The School Locker)
Williams, G. (2011). Data mining with Rattle and R: The art of excavating data for knowlegde discovery (recommended)
Larose, D. T. (2005). Discovering Knowledge in Data: an Introduction to Data Mining (recommended)
Submission of assessment items
Extensions & Late submissions
Late submission of assignments
Late submission of assignments without an approved extension will result in the assignment not being marked and zero being recorded for that particular assignment.
Extensions: Extensions must be applied for before the due date.
Students can apply for an extension to the submission due date for an assessment item on the grounds of illness or other unavoidable and verifiable personal circumstances. Documentary evidence will be expected for an extension to be granted.
It should should be noted that such documentation will be considered but will not guarantee that the application will be successful. The Unit Convener will decide whether to grant an extension and the length of the extension.
Special assessment requirements
An aggregate mark of 50% is required to pass the unit.
Your final grade will be determined as follows:
Final mark (100%) = Quiz 1 (5%) + Quiz 2 (10%) + Quiz 3 (10%) + Quiz 4 (15%) + Assignment (35%) + Presentations and Reflection (25%)
High Distinction (HD)
A total workload of 150 hours include 24 hours of lectures, 20 hours of tutorials, 20 hours of preview/review time for lectures and tutorials, preparation and attempt time of 5 hours each for the 4 quizzes (total 20 hours), and 60 hours for assignment and 6 hours for preparing for presentations and comprehensive reflection. The stated hours include the time required to attempt/present assessment items.
Your participation in both class and online activities will enhance your understanding of the unit content and results in a better learning experience and achievement. Lack of participation may result in your inability to satisfactorily pass assessment items.
Required IT skills
Report writing skill and basic computer use as well as exposure to programming is assumed as the statistical programming language R (with Rattle GUI) will be used for the lab activities.
This unit involves online meetings in real time using the Virtual Room in your UCLearn teaching site. The Virtual Room allows you to communicate in real time with your lecturer and other students. To participate verbally, rather than just typing, you will need a microphone. For best audio quality we recommend a microphone and speaker headset. For more information and to test your computer, go to the Virtual Room in your UCLearn site and 'Join Course Room'. This will trigger a tutorial to help familiarise you with the functionality of the virtual room.
Textbook purchase and some printing costs are anticipated.
Work placement, internships or practicums
Not applicable to this unit.
Provision of information to the group
Communications and announcements throughout the term will be made to the whole class through Canvas Announcements or the Canvas Discussion Forums. It is the responsibility of the student to ensure that they check for announcements on the unit's Canvas website. Students should ensure they check their student email regularly. The Discussion Forum will be checked by staff regularly.
Use of student email account
The University Email policy states that "students wishing to contact the University via email regarding administrative or academic matters need to send the email from the University account for identity verification purposes". Therefore all unit enquiries should be emailed using a student university email account. Students should contact firstname.lastname@example.org if they have any issues accessing their university email account.
In all cases of absence, sickness or personal problems it is the student's responsibility to ensure that the Unit Convener is informed. The minimum participation requirement must be met in order to pass the unit (regardless of supporting documentation).
- 8697 Data Analytics and Business Intelligence PG.
- Semester 2, 2020, On-Campus, UC - Canberra, Bruce (195583)
- Semester 1, 2020, On-Campus, UC - Canberra, Bruce (198344)
- Semester 2, 2019, On-Campus, UC - Canberra, Bruce (192649)
- Winter Term, 2019, On-Campus, UC - Canberra, Bruce (185604)
- Semester 2, 2018, On-Campus, UC - Canberra, Bruce (190349)
- Winter Term, 2018, On-Campus, UC - Canberra, Bruce (182354)