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