1 day course 
Private 
Certificate of Attendance Data can be a powerful tool, but with great power comes great responsibility, and how you collect, manage, and apply data can have profound ethical implications for your business.
In this course we cover ethical considerations across each point of the data life cycle, helping you to use data responsibly, fairly and transparently. Our analytics experts will give you the skills and know-how to lead conversations in essential data ethics topics such as privacy, accountability, and appropriate use as well as in hot-button and ever-evolving topics such as inclusivity, sustainability and the use of AI.
Throughout the session, you’ll engage in practical exercises, group discussions, and guided self-assessments to help identify your values, blind spots, and obligations when working with data.
By presenting theory, case studies and suggested approaches, we’ll inspire you to develop a data ethics framework of your own and take the first steps to embedding ethical thinking within your organisation.
This Data Ethics course is available as a private session that can be delivered via Virtual Classroom, at our training centre in The Shard, London, or at a location of your choice across the UK.
Course overview
Who should attend:
This course is perfect for anyone who works directly or indirectly with data; from analysts and data scientists to decision makers and those helping produce or communicate data-informed insights to their teams.
What you'll learn:
By the end of this course, you will be able to:
- Identify risks and considerations inherent to the collection of data
- Understand the social and legal impact of data use and misuse
- Recognise and mitigate sources of bias throughout the data lifecycle
- Begin drafting a living Data Ethics Framework tailored to your projects, people, and purpose.
Course agenda
- The data lifecycle
- Know the rules where you operate
- Developing your data ethics framework
- Data privacy and security considerations
- Ethical data collection
- Establishing accountability
- Assessing impact
- Identifying and mitigating bias
- Inclusive data practices
- Green data
- Data quality and limitations
- Using models, algorithms and AI
- Data communication