When I first came across Excel, it honestly felt like just another program filled with endless rows, columns, and unfamiliar formulas. At that point, I didn’t really understand what it was meant for beyond simple calculations. I could add numbers, format a table, and that was about it. Everything else felt overwhelming and a bit intimidating. Looking back, that was the beginning of a journey that completely changed how I think about and work with data.
For the past month, I have been exploring data analysis using Excel. Excel is something I’ve seen used almost everywhere—in businesses, schools, and even in small personal projects. Over time, I’ve come to appreciate that it’s not just a spreadsheet tool. It’s more of a way to take raw, messy data and turn it into something that actually makes sense.
The Early Days: Struggling to Understand the Basics
In the beginning, things didn’t come easily. Writing formulas correctly or even referencing cells felt confusing. I remember making simple mistakes like missing brackets or getting errors that I didn’t fully understand.
The first functions I learned were things like SUM() and AVERAGE(). Even though they looked straightforward, it still took time to get comfortable using them. At that stage, I wasn’t just learning Excel—I was slowly learning how to think in terms of structured data.
Discovering the Power of Logic
As I continued practicing, I started using IF statements. That was a real turning point for me.
For the first time, Excel felt like more than just a tool for calculations. It could actually make decisions based on conditions. I could categorize values automatically instead of doing it manually. For example, labeling results as “Poor,” “Average,” or “Excellent” depending on the value.
That’s when it started to make sense that Excel isn’t just about storing data—it can interpret it too.
Learning to Work with Messy Data
One thing I quickly realized is that real-world data is rarely clean. I often came across missing values, inconsistent formatting, duplicates, and text that needed fixing.
To deal with this, I started using tools like:
Remove Duplicates
Text to Columns
TRIM
Basic techniques for handling blanks and errors
This part of the learning process taught me something important: before you analyze anything, you have to make sure the data itself is clean and reliable. Otherwise, the results won’t be meaningful.
Applying Excel to Real Data: The Jumia Dataset Experience
One of the most important parts of my learning journey was working with a real dataset from Jumia.
At first glance, it looked like a simple table of products with details like prices, discounts, ratings, reviews, and categories. But as I spent more time with it, I realized it represented something much bigger—a live marketplace where products compete, and customers express their opinions through ratings and reviews.
Each row represented a product, while the columns showed different attributes such as:
Product categories
Prices and discount percentages
Customer ratings
Number of reviews
However, the dataset wasn’t ready to use straight away. There were missing values, inconsistencies, and formatting issues that needed to be fixed first.
Cleaning the data took time and attention. I had to handle missing ratings, standardize values, remove inconsistencies, and make sure everything was structured properly before moving on to analysis.
Seeing Patterns in the Data
Once the data was clean, I started exploring it using PivotTables, charts, and conditional formatting.
At that point, things started to become more interesting. I began to notice patterns such as:
Products with more reviews often had more stable ratings
Some categories consistently performed better than others
Discounts seemed to influence how products were engaged with
Missing ratings pointed to gaps in the dataset
Instead of looking at individual products one by one, I started seeing relationships between pricing, ratings, and categories.
Turning Data into Insights
As I continued analyzing, the dataset started to tell a clearer story.
It became easier to see that:
Customer ratings and reviews play a big role in product performance
products with high discounts behave differently when it comes to engagement
Discounts may affect how products are perceived
Missing or incomplete data can limit the quality of insights
This experience helped me understand that Excel is not just for calculations—it’s a tool for uncovering patterns and making sense of real-world situations.
Building Dashboards
As I improved, I began combining different Excel features to build dashboards.
These dashboards included:
Charts to visualize trends
PivotTables for summaries
Conditional formatting to highlight important values
Key metrics that made the data easier to understand
This helped me move from working with raw data to presenting information in a way that is clear and easy to interpret.
Why Excel Matters to Me
Excel has become a very practical tool in my work and learning. I’ve seen how it can be used in different areas such as:
Financial analysis and budgeting
Sales and marketing analysis
Data cleaning and organization
Project tracking and management
Some of the features I rely on most include PivotTables, IF statements, VLOOKUP/XLOOKUP, charts, and conditional formatting. Each of these helps me work with data more efficiently and make better sense of it.
Final Thoughts
Looking back, learning Excel has changed the way I approach data. I no longer just see numbers—I look for patterns, relationships, and meaning behind them.
The moment I realized Excel could go beyond simple calculations was a big shift for me. It felt like I had uncovered another layer of understanding in the data. Numbers started to feel more like information that tells a story.
That’s really why I enjoy working with Excel. It helps turn raw data into something useful, and eventually into decisions that actually make sense.
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