ADS1001 · Data challenges 1

Overview

This is the first in a series of data challenges units which collectively develop a broad range of knowledge and transferable skills through studio-based learning, applied problem-solving and the exploration of a broad diversity of cross-disciplinary and industry-relevant data science case studies over the course. In recent years the world has seen an explosion in the quantity and variety of data routinely recorded and analysed by research and industry. The data may come from a variety of sources, including scientific experiments and measurements, legal documents, archives, human interactions such as browsing data or social networks on the Internet, mobile phone usage or financial transactions. Data science provides the analytical and visualisation techniques required by practitioners to obtain insights into their data. This inquiry-based boot camp unit will include an introduction to important elements of data science, how it is impacting on society, and the role it will play in addressing problems and issues across the sciences, business arena and industry. You will be exposed to the characteristics of data science over and above the core task of data analysis. Through interdisciplinary team-based workshops you will begin to collaboratively explore examples of complex problems which have been solved through the fusion of data science, mathematics and statistics, social, business, IT and interdisciplinary knowledge. You will apply the key principles, tools and techniques of data science to authentic problems and implement approaches and solutions, communicating outputs effectively for a range of stakeholders.

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. Identify the principles of scientific thinking and apply them in the context of data science;
2. Reflect upon how to create and deliver results in interdisciplinary teams;
3. Critique the ethical and multicultural dimensions associated with data science decisions, use and quality and their possible impacts on organisations and society;
4. Communicate outcomes effectively in a range of formats including orally, visually and in written form;
5. Identify the various steps to perform data analysis and visualisation;
6. Explore the importance of data in a variety of fields including science, IT and business.

Workload

• Three hours of online learning to be completed pre-workshop; • One three-hour workshop and • Approximately six hours of project work and reflective practice.

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