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 … For more content click the Read More button below.
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
S2-01-CLAYTON-IMMERSIVE
Rules
Enrolment Rule
Contacts
Chief Examiner(s)
Dr Simon Clarke
Unit Coordinator(s)
Dr Simon Clarke
Learning outcomes
On successful completion of 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.
Teaching approach
Active learning
Assessment
1 - Assignments
2 - Reflective journal
3 - Supervisor report
Scheduled and non-scheduled teaching activities
Seminars
Workload requirements
Workload
Off campus attendance requirements