After receiving my hospital records dataset and understanding its structure, the next step was data cleaning.
Before performing any analysis, it is important to ensure that the data is accurate, consistent and ready to produce reliable insights.
For this phase of the project, I worked on preparing the dataset using Microsoft Excel.
Preserving the Original Data
One important practice I learned is to always keep the original dataset untouched.
I created a new worksheet named Cleaned Data by copying the original records. All cleaning activities were performed on this new worksheet while keeping the raw data available for reference.
Data Cleaning Activities
The following steps were performed:
✅ Checked for duplicate records
- Reviewed the dataset for duplicate entries.
- No duplicate records were found.
✅ Checked for missing values
- Examined all columns for blank cells.
- No missing values were identified.
✅ Improved data formatting
- Converted the Phone Number column into a numerical format.
- Formatted the Bill Amount column into Kenyan Shillings (KSh).
✅ Created a Full Name column
To improve readability, I combined the First Name and Last Name columns using Excel's CONCAT function:
=CONCAT(B2," ",C2)
After verifying the results, the original First Name and Last Name columns were hidden since the information was now represented in the Full Name column.
✅ Improved worksheet readability
- Adjusted column widths to ensure all data was displayed clearly.
What I Learned
This phase reminded me that data analysis is not only about creating charts or writing queries. The quality of insights depends heavily on the quality of the data being analyzed. Taking time to clean and document data creates a strong foundation for the next stages of the project.
What's Next?
The next phase will be Exploratory Data Analysis (EDA), where I will start using Excel formulas, PivotTables, and charts to uncover patterns and insights from the hospital records.
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