The British Academy for Training and Development offers this training program on Data Preparation and Analysis, designed to develop participants’ skills in collecting, organizing, and analyzing data scientifically to support decision-making across various work environments.

Data analysis has become one of the most essential modern tools for understanding trends, uncovering relationships, and providing practical solutions to administrative and operational challenges—especially with the increasing volume and diversity of information sources.
This program introduces key concepts and methods used in data management, from preparation and processing techniques to applying quantitative and qualitative analytical tools. It also equips participants with the ability to interpret results and present them professionally, enhancing the value of analytical outputs and strengthening the link between analysis and decision.

Who should attend? 

• Employees working in planning and development departments in public and private institutions.

• Data analysts and professionals in management information systems.

• Research and studies officers, and teams in data and statistical centers.

• Anyone wishing to develop data analysis skills for professional purposes.

Knowledge and Benefits:

After completing the program, participants will be able to master the following:

• Understand the basic principles of data preparation and analysis.

• Acquire skills in organizing, cleaning, and preparing data for analysis.

• Use statistical analysis tools to understand patterns and indicators.

• Produce precise and professional analytical reports.

• Support decision-making through data-driven analysis.

• Introduction to Data Science

  • Definition of data science and its importance in organizations

  • Differences between data, information, and knowledge

  • Areas of application for data analysis

• Types of Data and Their Sources

  • Quantitative and qualitative data

  • Data sources: primary and secondary

  • Challenges in collecting data from multiple sources

• Data Collection Methods

  • Designing data collection tools (surveys, interviews)

  • Ethical considerations in data collection

  • Electronic data collection methods

• Data Organization and Coding

  • Creating and structuring spreadsheets

  • Coding values and transforming variables

  • Handling missing values and duplicates

• Preparing Data for Analysis

  • Data cleaning and error correction

  • Formatting data types and converting structures

  • Merging data from different sources

• Software Tools for Data Analysis

  • Introduction to Excel as a primary analysis tool

  • Overview of SPSS, Power BI, and Python

  • Criteria for selecting the appropriate analytical tool

• Descriptive Statistical Analysis

  • Mean, median, and standard deviation

  • Frequency distributions and graphical measures

  • Using tables and charts to interpret results

• Trend and Indicator Analysis

  • Extracting patterns from data

  • Identifying temporal changes and trends

  • Linking indicators with actual outcomes

• Relationships Between Variables

  • Correlation and regression analysis

  • Interpreting cause-and-effect relationships

  • Applying analysis in practical scenarios

• Qualitative Data Analysis

  • Handling open-text responses

  • Categorizing and coding qualitative answers

  • Extracting key themes and concepts

• Data Visualization and Presentation

  • Choosing the appropriate chart type

  • Using colors and design principles in visualization

  • Transforming numbers into meaningful stories

• Developing Analytical Reports

  • Components of a professional analytical report

  • Writing with analytical clarity and precision

  • Providing recommendations based on results

• Data-Driven Decision-Making

  • Linking analysis to decision-making stages

  • Evaluating alternatives based on evidence

  • Measuring the impact of data-informed decisions

• Performance Indicators and Their Analysis

  • Definition and types of KPIs

  • Analyzing gaps between targets and actual results

  • Creating performance dashboards

• Continuous Improvement Through Analysis

  • Using analysis to enhance processes

  • Monitoring performance over time

  • Forecasting and improving problem response

• Future Trends in Data Analysis

  • Artificial intelligence in predictive analytics

  • Big Data and its significance

  • Integrating analysis with emerging technologies