Course Modules

Module 1: Introduction to Data Management Concepts

• Importance of clean and well-managed data
• Overview of data lifecycle (collection to storage)
• Understanding file formats: CSV, XLSX, SAV
• Metadata and documentation basics
• Case Study: Poor data management and its effect on policy reporting

Module 2: Common Data Errors and Cleaning Techniques

• Identifying errors: typos, duplicates, missing values
• Using filters and validation rules in Excel
• Recoding variables and fixing inconsistent entries in SPSS
• Removing outliers and irrelevant data
• Case Study: Cleaning health survey data for reliable reporting

Module 3: Working with Structured Datasets in Excel

• Formatting cells and data types
• Sorting, filtering, and conditional formatting
• Using formulas for quality checks (IF, ISBLANK, COUNTIF)
• Splitting and merging datasets
• Case Study: Using Excel to prepare a dataset for education statistics

Module 4: Data Cleaning in SPSS

• Importing data into SPSS
• Labeling variables and managing value labels
• Handling missing data with SPSS tools
• Running frequency checks and cross-tabs for validation
• Case Study: SPSS cleaning for a national labor force dataset

Module 5: Finalizing and Storing Cleaned Datasets

• Backups and version control for datasets
• Creating clean and analysis-ready files
• Best practices in organizing folders and archives
• Sharing data for reporting and collaboration
• Case Study: Organizing statistical data for quarterly government performance review