Student Exam Data for Analysis

Requirements Clarification & Assessment

Before diving into the solution, it's crucial to clarify and assess the requirements and limitations of the current data organization:

1. Understand the Data Formats:

   • Dataset A: This dataset may have issues with misaligned names and missing values, making it challenging to interpret.
   • Dataset B: This is a pivoted table with unique names as column headers, which is unconventional and complicates relational analysis.

2. Identify the Observational Unit:

   • Determine if the observational unit is a student, an exam, or a combination of both. This will guide the restructuring process.

3. Data Consistency:

   • Ensure that the data is consistent, with a clear understanding of what missing values represent.

4. Scalability:

   • The datasets should be scalable to accommodate new students and exams without creating sparse data structures.

5. Analytical Goals:

   • Clearly define the analytical goals, such as trend analysis, comparison across students, or performance metrics, to guide data restructuring.

6. Existing Issues:

   • Identify common issues in the datasets, such as multiple variables in one column, column headers as values, and missing data handling.