ADS3001 Advanced data challenges
Faculty of Science
ADS3001 Advanced data challenges is a level 3, 12-credit-point, undergraduate unit from the Faculty of Science, offered in 2022 in Semester 2 at Clayton. It needs ADS2002 .
- Credit points
- 12
- Offered in 2022
- Semester 2
- Clayton
- Assessment
- No exam
- 3 tasks
This is the 2022 handbook entry. See the 2027 entry.
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Requisites
Before ADS3001
Prerequisites
Pass these before you enrol.
- ADS2002 Data challenges 4
After ADS3001
No unit lists ADS3001 as a prerequisite in the 2022 handbook.
Overview
This is the final in a series of Data Challenges units which draws together your mathematical, computational and applied studies, and builds on the industry-relevant data science case studies explored during the first two years of the Bachelor of Applied Data Science. You will apply this knowledge working in industry and academic placements.
You will further develop and apply your analytic and technical skills to interrogate and understand large and complex real-world data sets drawn from academic, governmental and business problems. You will continue to develop your communication skills through a combination of written, oral and multimedia presentations, which communicate your analysis and conclusions to a range of potential stakeholders. Finally, you will work in teams to enhance your project management, collaborative and leadership skills.
The placements will embed you in data science teams in a range of government, industry and academic settings. These placements will be complemented by weekly seminars to provide insight into real problems faced by experts in the field.
Offerings in 2022
| Teaching period | Campus | Mode |
|---|---|---|
| Second semester | Clayton | On campus |
Assessment
- AssignmentsAssignment70%
- Reflective journalOther20%
- Supervisor reportReport10%
Learning outcomes
When you finish this unit, you should be able to:
- 1
Critically analyse data-oriented projects to break these down into achievable tasks;
- 2
Demonstrate the ability to work in a team to plan and complete a complex data-orientated project;
- 3
Analyse the ethical issues associated with data science decisions that arise;
- 4
Clearly communicate complex ideas to potential stakeholders using a variety of approaches;
- 5
Effectively manipulate, analyse and visualise data;
- 6
Implement a range of advanced machine learning algorithms;
- 7
Undertake independent research on data science techniques and relevant domain knowledge.
Workload and teaching
- Seminars12 hours
- Teaching approachActive learning
- Two days (12 hours) of placements and
- Approximately twelve hours of attendance at seminars, independent project work and reflective practice per week
As this is the capstone unit for the Bachelor of Applied Data Science, the primary objective is to apply the learning from throughout the course and therefore the most appropriate learning method is active learning. This is achieved through the two forms of activity, placements and weekly seminars.
You will be embedded in organisational teams and undertake active learning. You will apply your accumulated knowledge to work in teams, plan and complete projects, interrogate large and complex datasets, implement advanced machine learning algorithms on the data, research new data science techniques to apply to your project, deal with any ethical issues associated with the data science decisions and communicate their results to a range of stakeholders through written and oral presentations (LO1-LO7). Furthermore, they will be exposed to a range of techniques and issues specific to their placement (e.g., LO2, LO3, LO4, LO7).
Contacts
- Unit Coordinators
- Dr Simon Clarke
- Chief Examiners
- Dr Simon Clarke
Common questions
What are the prerequisites for ADS3001?
You need ADS2002 before you enrol.
When is ADS3001 offered?
In 2022, ADS3001 runs in Semester 2 at Clayton.
Does ADS3001 have an exam?
No. ADS3001 has 3 assessment tasks and no exam.