The Future of Data – Data Analytics: Concepts, Principles and Practices

Digitalization has vastly increased the availability, use, and value of data. HDR candidates must be enabled to engage in the effective, efficient, and ethical use of data for research purposes. This module develops participants fundamental awareness and knowledge about key concepts, essential tools, best practices across the full lifecycle of data-driven research.

This course is designed to shape how you think, feel and act in relation to data. The goal is to increase your confidence and ability to navigate and prepare for the demands of professional roles in our data-driven world. This module helps you develop a fundamental awareness and understanding about key concepts, popular tools, and best practices as they relate to data.

Knowing where to start can be a daunting task. This module provides a series of self-directed learning experiences and facilitated workshops designed to increase your awareness and adoption of useful practices and principles for the effective, efficient, and ethical use of data.

Successfully completing this module means you can better handle better data for meaningful outcomes in your research and professional practice.

Module Themes:

The learning experience onboards every learner by explaining the learning goals and module structure, it sets and manages expectations early, and suggests principles to succeed. [self-paced]

The module scaffolds over 8 topical categories that engage the learner in a logical sequence of self-paced and/or blended learning workshop sessions:

1. Concepts, Drivers & Trends: Defining key ideas and principles relating to data, analytics, and modelling. [self-paced]
2. Privacy, Security, Ethics. [self-paced]
3. Responsible Data Management [self-paced]
4. From Question to Data to Insights. [self-paced]
5. Data Analytics Toolkit: Introducing principles, aids and insights for coding and analytics by syntax. [self-paced]
6. Start to Code: Overview and hands-on Python, markdown, Jupyter notebooks, Kaggle. [blended: self-paced + interactive workshop]
7. Explore, Describe, Visualise: Convert and communicate data through graphic representation. [blended: self-paced + interactive workshop]
8. Tests, Model, Predict: Demonstrating basic analytical computations using the Analytics Toolkit. [blended: self-paced + interactive workshop]

Learning Outcomes:

Upon completing this module, students should be able to:

• Understand key concepts, drivers and trends as they relate to data analytics as a phenomenon and profession
• Demonstrate more autonomy and authoritative judgement toward responsible and effective data management
• Carry out basic yet powerful data transformations in Python and confidently continue to grow your skills

Who Should Enrol?

This module is suitable for all Higher Degree by Research students of all disciplines.

2023 Delivery Dates