DEV Community: Dennis Wachira

# Cleaning My Hospital Records Dataset: Preparing Data for Analysis

Dennis Wachira — Wed, 28 Oct 2026 19:03:52 +0000

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.

Dataset

Dennis Wachira — Tue, 27 Oct 2026 09:31:20 +0000

Today I officially started working with my Hospital Records Analysis dataset.

Receiving the dataset marked the beginning of a project that I’ll be using to strengthen my skills in Excel, SQL, and Power BI. Instead of jumping straight into analysis, I’m taking the time to understand the data and build good habits from the start.

My plan for this project is to:

Explore and understand the dataset
Clean and prepare the data in Excel
Write SQL queries to answer business questions
Build an interactive Power BI dashboard
Document every step on GitHub

I’m treating this like a real-world analytics project, focusing not only on finding insights but also on following a structured workflow and documenting my progress.

Looking forward to sharing what I learn as I continue building this project.

Git & GitHub

Dennis Wachira — Wed, 09 Sep 2026 20:34:42 +0000

Recent Milestones

One of my biggest achievements so far has been learning Git and GitHub. After setting up Git on my Mac, I successfully connected it to my GitHub account and pushed my very first project to a remote repository. Along the way, I have learnt how to navigate the terminal, create project folders, initialize Git repositories, create and edit README.md files, stage changes, commit my work with meaningful messages, and push my code to GitHub.

These may seem like small steps, they have given me a solid foundation in version control—an essential skill for every data analyst and developer. Every command I learn brings me one step closer to becoming more confident in managing projects and collaborating with others.

My Roadmap to Becoming a Data Analyst

Dennis Wachira — Tue, 08 Sep 2026 19:42:36 +0000

Introduction

My name is Dennis, and I'm excited to share my very first article on DEV. Through this blog, I'll be sharing what I learn, the challenges I encounter, the projects I build, and the lessons I pick up throughout the journey. My hope is that my experiences will encourage and help others who are also taking their first steps into the world of data.

My Data Analytics Toolkit

As I begin my journey into data analytics, I've chosen to focus on a set of tools that are widely used across the industry. Each one plays a unique role in the data analysis process, and together they form the foundation of my learning roadmap. They are the following;

Excel - Data cleaning, organization, and analysis.
SQL - Querying and managing data from databases.
Python - Data manipulation, automation, and analysis.
Git and GitHub - Version control and project collaboration.
Power BI - Transforming raw data into meaningful dashboards and reports.
Microsoft Fabric - Brings together data engineering, data science, data warehousing, and business intelligence.

These tools represent more than just software—they're the building blocks of my journey toward becoming a skilled data analyst. I'm looking forward to learning each one step by step, applying them to real projects, and sharing what I discover along the way.