AbdulsamadMurt/Student-Data

As part of the DLP Year 2 executive report, I cleaned, validated, and organized student data from seven northern states in Nigeria. The task involved comparing enrollment data from KoboToolbox with assessment records to ensure accuracy and consistency. Project Title: Student Data Cleaning and Validation for DLP Year 2 Assessment Project Description: As part of the executive reporting process for the DLP Year 2 program, I was responsible for cleaning, validating, and organizing student data collected from multiple sources across seven northern states in Nigeria. The task involved comparing two key datasets — the student enrollment data (collected via KoboToolbox during registration) and the assessment result data (recorded during student assessments). The primary goal was to ensure data consistency and integrity by confirming that the students registered during the program were the same individuals who participated in the assessments. Key Responsibilities: Extracted raw student data from the KoboToolbox dataset and formatted it to match the structure of the assessment dataset. Cleaned and validated the data by identifying and removing duplicate entries, correcting typographical errors, and eliminating outliers (e.g., invalid names or numeric anomalies). Segregated and organized data into separate Excel sheets for each of the seven northern states, ensuring uniform formatting and completeness. Performed comparative analysis to identify: Variations between the enrollment and assessment datasets. Additional or missing student records. The number of learners who passed or failed the assessments. Ensured data accuracy and consistency to support certificate processing and executive decision-making. Outcome: A comprehensive, well-structured, and cleaned student dataset was produced for all seven states. The final report enabled the data team to confidently proceed with certification processing and performance analysis for Year 2 learners. Tools & Technologies Used: Microsoft Excel (Data Cleaning, Validation, and Formatting) KoboToolbox (Data Source and Verification) Data Analysis Techniques: Duplicate detection, outlier removal, data merging, and consistency checking