Course Overview
In today’s data-driven world, the ability to analyze, interpret, and present data effectively is essential for professionals in project management, M&E, research, and planning. This course provides participants with the advanced skills needed to transform complex data into clear, meaningful insights and compelling presentations using widely recognized tools, including SPSS, STATA, and R.
With data volumes growing exponentially, professionals must go beyond basic spreadsheets and charts to uncover patterns, make predictions, and guide strategic decisions. This training delivers a practical, hands-on experience in both statistical analysis and high-impact data visualization techniques.
Two full days are dedicated to statistical analysis using R, one of the most powerful and widely used tools for data science, allowing participants to explore coding-based analysis and visualization.
Who Should Attend
- Program and Project Managers
- Monitoring & Evaluation Practitioners
- Data Analysts and Researchers
- Planners and Statisticians
- Postgraduate Students (Masters & PhD)
- Prospective Researchers and Scholars
Key Competencies Gained
By the end of this workshop, participants will be able to:
- Perform statistical analysis and present various types of data appropriately
- Identify opportunities to apply statistical methods to real-world challenges
- Visualize and communicate data findings clearly and persuasively
- Apply tools like R, SPSS, and STATA to analyze and present data
- Develop mathematical models based on real data
- Advise decision-makers using evidence-based insights
- Build a portfolio of data projects demonstrating practical applications
- Ensure data ethics, quality, and governance in all stages of analysis
Course Content Overview
Day 1 – Foundations of Data Analysis and Presentation
- Types of data and measurement scales
- Statistical concepts and analytical frameworks
- Fundamentals of visualizing data effectively
- Principles of clarity, accuracy, and integrity in data presentation
Day 2 – Modern Data Visualization Techniques
- Graph types to avoid and new graph alternatives
- Choosing the right chart or graph for your data
- Best practices in designing effective visualizations
- Creating charts that tell a compelling story
Day 3 – Tools for Data Collection, Cleaning, and Visualization
- Data cleaning techniques and handling missing data
- Using SPSS and STATA for graphical analysis
- Ethical considerations and ensuring data privacy
Day 4 – Introduction to R for Data Science
- Installing and setting up R
- Using R for data analysis and advanced visualization
- Interactive data presentation using R’s graphics capabilities
Day 5 – Communicating Data Insights
- Turning data into decisions
- Hands-on practice and project-based exercises
- Developing and presenting a portfolio of analysis projects
- Introduction to data governance and maintaining data quality
Training Approach
The course uses interactive, participant-centered, and practical adult learning methods. Sessions combine expert instruction, hands-on exercises, and real-world case studies to ensure participants leave with both knowledge and confidence.
Free follow-up support and coaching will be available for all interested participants after the training