Course Dates, Location and Format
Dates: 21 September – 02 October 2026
- Format: In-person
- Location: Rewley House, Oxford
- Duration: 10 days
This course is ideal for:
- Government officials and public servants at local, regional or national level
- NGO professionals working in public service delivery
- Mid-level managers responsible for programmes, policy or strategy
- Professionals in data, data-adjacent, or data-informed roles
It is particularly valuable for those with hands-on programme experience who work with large volumes of operational or programme data.
This course will help you:
Learning Experience
Participants will benefit from a highly interactive learning experience that includes:
- Expert-led sessions grounded in public sector realities
- Global case studies from both high-income and low- and middle-income contexts
- Group discussions and peer learning
- Practical exercises using realistic data scenarios and tools
Daily schedule
Week 1: 21 – 25 September 2026
The first part of the course is dedicated to modern data concepts and data governance.
Monday: Setting the landscape
Tuesday: Data-driven decision-making
Wednesday: Data governance
Thursday: Technological landscape for data
Friday: Human resource for data and data governance
Click here for the daily timetable.
Week 2: 28 September – 02 October 2026
The second part of the course is dedicated to capacity-building on data management.
Monday: Data privacy, security, and protection
Tuesday: FAIR principles of data management
Wednesday: All about spreadsheets
Thursday: Project-based workflows
Friday: Summing up
Click here for the daily timetable.
By the end of the course, you will be able to:
- Use data confidently to support evidence-based decision-making
- Understand modern data governance principles and why they matter
- Choose appropriate data tools and technologies for your organisation
- Apply FAIR data principles to improve data quality and reuse
- Use spreadsheets effectively while understanding their limitations and risks
- Reduce technical and technological debt through smarter data practices