Data and Statistical Analysis for Public-Sector Decision-Making

Dates:

• 17 - 21 May 2027

• 22 - 26 November 2027

Course Outline

Public-sector organisations need reliable evidence to design policy, allocate resources, improve services and demonstrate accountability. This workshop helps managers and practitioners make confident use of data from operational systems, surveys, research and open-data sources, while recognising the importance of quality, context, transparency and public trust.

Using practical public-sector scenarios, delegates will follow the analytical process from defining a decision and selecting suitable data through to analysis, interpretation and communication. Activities introduce descriptive statistics, performance measures, visualisation, forecasting and scenario testing using accessible spreadsheet and analytical tools. The workshop also addresses data governance, uncertainty, bias, reproducibility and the responsible use of automation and artificial intelligence. No previous statistical training is required.

Course Objectives: 

On completion, you will be able to:

• Define a clear decision, analytical question and measure of success
• Identify appropriate organisational, research and open-data sources
• Assess data quality, relevance, limitations and governance requirements
• Prepare and summarise data using suitable statistical techniques
• Use visualisation, forecasting and scenario analysis to explore options
• Interpret findings responsibly, including uncertainty, bias and ethical considerations
• Communicate clear conclusions and proportionate recommendations to decision makers

Key Topics

• The role of evidence and statistical thinking in public-sector decisions
• Turning policy and operational priorities into answerable questions
• Administrative, survey, research and open-data sources
• Data quality, metadata, governance, privacy and security
• Cleaning, combining, transforming and validating data
• Descriptive statistics, distributions, variation and uncertainty
• Sampling, comparisons, correlation and common analytical pitfalls
• Performance indicators, baselines, targets and benchmarking
• Spreadsheet analysis, pivot tables and reproducible workflows
• Simple forecasting, scenario testing and sensitivity analysis
• Responsible use of automation, machine learning and generative artificial intelligence
• Choosing clear charts and presenting meaningful management information
• Explaining findings, limitations and recommendations to different audiences