68028 Data Literacy: Data Informed Decision Making

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Subject handbook information prior to 2024 is available in the Archives.

Description

In today's world, people are increasingly asked to assess risk and make decisions in situations with uncertain information. In this course, students learn essential quantitative literacy and critical thinking skills required to interpret data and inform decision making.

Drawing on data science methods in statistics and probability, students explore how to evaluate quantitative information used as evidence, as well as how cognitive bias can creep into decision making situations.

Teaching and learning strategies

The course is taught in fully online mode. Participants will complete self-paced learning supported by formative assessment tasks to develop their skills and knowledge. Participants will have the opportunity to present to a facilitator their business context of choice prior to developing the case study for their final project.?Each week the key course fundamentals are taught with self-contained examples, then applied to a broader case study which is extended throughout the course. The participants then complete the component of the assessment that will apply to their own case studies, so that by the end of session, the assignment requires small collation prior to submission.

Content (topics)

This course promotes development of numeracy, quantitative literacy and critical thinking skills. Informed citizens need these skills to participate in discussion of significant issues in culture and society. The focus of this course is on the use of quantitative information as evidence to inform decision making, including risk and uncertainty.

Participants will apply their skills to identify a scenario within their own professional or disciplinary contexts in which a decision must be made based on uncertain information. Participants will consider the kinds of evidence available in these contexts, how this evidence informs such decisions, and the potential for cognitive bias in making these decisions.

Modules in this course include:

Module 1: Understanding Stakeholders

• Describe the kinds of decisions that would benefit from a quantitative data analysis
• Identify the stakeholders involved in an issue and describe their positions with respect to the issue
• Identify and explain potential biases in framings of a situation or in stakeholders’ framing of a situation

Module 2: Types of Evidence

• Create a risk matrix and translate this into probabilities
• Calculate an expected utility of a risk situation
• Analyse the utility function graphs for risk averse, risk neutral and risk seeking individuals and where stakeholders may be located on this function
• Apply Bayesian judgement based on prior information of a situation

Module 3: Using Data and Evidence to Support a Decision

• Construct a decision tree and calculate the rollback to assist decision making
• Incorporate the impact of time on the value of potential outcomes
• Communicate recommendations to decision makers based on evidence

Minimum requirements

Students must pass all components.

Attendance at synchronous sessions is compulsory.