Blending theory with hands-on practice, the course focuses on collecting, analyzing, visualizing, and drawing meaningful conclusions from data using accessible tools and techniques.

By the end of this course, participants will be able to:

• Understand types of data and basic statistical concepts
• Apply various methods for data cleaning, analysis, and visualization
• Interpret data accurately and communicate insights effectively
• Use software tools (Excel, Power BI, or SPSS/R/Python based on audience)
• Make informed, data-driven decisions

This course is ideal for:

• Data analysts and researchers
• Business and financial professionals
• Project managers and evaluators
• NGO and government staff involved in monitoring and evaluation (M&E)
• Anyone who works with reports, data, or decision-making

This interactive Training will be highly interactive, with opportunities to advance your opinions and ideas and will include:
• Lectures
• Workshop & Work Presentation
• Case Studies and Practical Exercise
• Videos and General Discussions

Day 1: Introduction to Data Analysis

• Types and sources of data (quantitative vs. qualitative)
• Data collection methods and best practices
• Overview of data analysis process
• Introduction to descriptive and inferential statistics
• Common data pitfalls and how to avoid them

Day 2: Data Cleaning and Preparation

• Data formatting, validation, and transformation
• Handling missing data, outliers, and duplicates
• Structuring datasets for analysis
• Using Excel or a selected tool (e.g., SPSS, Python, or R)
• Practical data cleaning exercises

Day 3: Statistical Analysis Techniques

• Descriptive statistics: mean, median, mode, standard deviation
• Correlation and regression analysis
• Hypothesis testing (t-tests, chi-square, ANOVA basics)
• Data segmentation and trend analysis
• Case-based exercises

Day 4: Data Visualization and Interpretation

• Principles of good data visualization
• Charts and graphs: when and how to use them
• Dashboards and storytelling with data
• Tools: Excel, Power BI, Tableau, or matplotlib/seaborn (Python)
• Interpreting results for reports and presentations

Day 5: Practical Applications and Final Workshop

• Real-world case study: analyze and interpret a full dataset
• Group presentations: data-driven decision recommendations
• Peer review and feedback
• Final wrap-up: best practices checklist and Q&A
• Course review and optional assessment/certification

A RECTUS attendance certificate will be awarded to all participants who complete at least 80% of the total course duration.