ADS3001 · Advanced data challenges

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

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

The Handbook lists no prerequisite, corequisite or prohibition for this unit.

Learning outcomes

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

• Two days per week (approx. 17 hours) of placements; • One hour of attendance at seminars and • Six hours of independent project work and reflective practice per week.

Student experience, not official rules. Nothing here changes what the Handbook says.