A spreadsheet full of numbers does not make a decision easier — it often makes it harder, until someone knows how to clean it, structure it, and ask it the right questions. That gap between raw data and a usable answer is where most organisations lose time and confidence in their own numbers.

This training course from the Arab British Fellowship Training Academy walks participants through the full journey data takes before it becomes useful: collecting it responsibly, preparing and cleaning it, applying the right statistical and software tools, and finally turning results into reports and decisions people can act on.

Who Should Attend?

• Employees in planning and development departments across public and private institutions.
• Data analysts and professionals working in management information systems.
• Research and studies officers and teams in data and statistical centres.
• Anyone looking to build professional data analysis skills.

By the End of the Course, You Will Be Able To:

• Collect, organise, and clean data using scientifically sound methods.
• Apply the right software and statistical tools for a given analysis task.
• Read and interpret patterns, trends, and relationships between variables.
• Produce clear analytical reports that support real decision-making.

• Module 1: Why Data Analysis Drives Better Decisions

  • The difference between data, information, and knowledge.
  • Where data analysis fits across different fields of work.
  • Linking analysis directly to the stages of decision-making.

• Module 2: Knowing Your Data — Types and Sources

  • Quantitative and qualitative data, and primary versus secondary sources.
  • Designing data collection tools such as surveys and interviews.
  • Ethical considerations and challenges in collecting data from multiple sources.

• Module 3: Getting Data Ready to Work With

  • Creating and structuring spreadsheets, and coding values.
  • Cleaning data, correcting errors, and handling missing values or duplicates.
  • Merging and formatting data from different sources.

• Module 4: Choosing the Right Software Tools

  • Excel as a primary analysis tool.
  • An overview of SPSS, Power BI, and Python.
  • Criteria for selecting the right tool for each type of analysis.

• Module 5: Descriptive Statistics for Everyday Use

  • Mean, median, and standard deviation.
  • Frequency distributions and graphical measures.
  • Using tables and charts to interpret results.

• Module 6: Finding Patterns — Trends and Relationships

  • Extracting patterns and identifying temporal trends.
  • Correlation and regression analysis.
  • Interpreting cause-and-effect relationships in practical scenarios.

• Module 7: Working with Qualitative (Text) Data

  • Handling open-text responses.
  • Categorising and coding qualitative answers.
  • Extracting key themes and concepts.

• Module 8: Telling the Story — Visualization and Reporting

  • Choosing the right chart type and using colour and design principles.
  • Components of a professional analytical report.
  • Writing with analytical clarity and providing evidence-based recommendations.

• Module 9: From Analysis to Action

  • Linking analysis to each stage of decision-making.
  • Defining and analysing KPIs, and creating performance dashboards.
  • Using analysis for continuous improvement and forecasting.

• Module 10: Where Data Analysis Is Headed

  • Artificial intelligence in predictive analytics.
  • The growing significance of Big Data.
  • Integrating analysis with emerging technologies.