When I started my Data Science and Analytics course a week ago, I expected to spend most of my time learning programming languages and building machine learning models.Instead one of the first tools we were introduced to was Microsoft Excel. At first, I wondered why a spreadsheet application was so important in a Data Science course, especially because I initially thought it was just something that accountants used. After just one week of learning it, I am starting to understand why.
So, what even is Excel? At its core, it is a spreadsheet tool where you work with rows, columns, and cells to store and organize data. That is the simple version. But after one week, I am starting to see that it is also a place where raw, messy data can actually start to make sense, and also how that data is eventually used in decision-making
Real-World Uses I Did Not Know About
1.Cleaning up messy data
One of the first things that surprised me was how much of data analysis is simply just fixing bad data. In real jobs, data comes in all shapes. Names typed in random cases, extra spaces everywhere, inconsistent formats. This week I learned PROPER(), UPPER(), and LOWER(), and I immediately thought: this is literally what analysts do before anything else. They clean. Removing duplicates is another one that sounds boring but is actually critical because one duplicate record in a sales dataset can throw off an entire report.
2.Tracking and summarizing numbers
Think about a small business owner who wants to know their average monthly sales, or a manager counting how many employees completed a training. Functions like SUM(), AVERAGE(), COUNT(), MAX(), and MIN() handle all of that. These functions seem simple at first glance but are actually very powerful when it comes to summarizing information very quickly to be used for creating reports and identifying trends. I practiced these on a sample dataset this week, that is the HR_dirty_data.xlsx dataset and it helped me understand the concepts even faster.
3. Filtering and validating data before analysis
Sorting and filtering are things I had used before without thinking much about them. But now I understand why they matter. Before you analyze anything, you need to see the right slice of your data. Filtering lets you do that without deleting anything. I also learned about data validation this week, which is used to control what gets entered into a cell in the first place. That is something I had never thought about before, but it makes a lot of sense for keeping datasets consistent.
Features That Stood Out to Me
Conditional formatting was probably my favorite discovery this week. You highlight cells based on their values, and suddenly a column of numbers becomes something you can read at a glance. I used it to flag values above a certain threshold and it felt genuinely useful, not just decorative.
Another concept that changed the way I think about spreadsheets was cell references. Rather than typing values directly into formulas, Excel allows you to reference cells so calculations update automatically whenever the underlying data changes.Here is an example i practiced:
=AVERAGE(B2:B10)
This formula calculates the average of the values between cells B2 and B10. What impressed me most was that if any of those values change, Excel automatically updates the result. That made me appreciate why analysts rely on cell references instead of manually recalculating everything.
Removing duplicates was another one I underestimated. In the exercise we did, there were repeated customer entries in a list. Finding and removing them manually would have taken forever, but with Excel's Remove Duplicates feature, the repeated records were removed in seconds, leaving a cleaner and more reliable dataset for analysis.
How This Week Changed the Way I See Data
Before this week, I thought data analysis was mainly about calculations and complex software. Now I realize that a large part of the process involves organizing, validating, and cleaning data before any analysis can happen.
Learning Excel has shown me that sorting, filtering, formatting, and cleaning are not just preparation steps. They are essential parts of working with data effectively. Even after only one week, I can already see why Excel remains one of the most widely used tools in data analysis.
I still have a lot to learn, but I am excited to continue building my skills and discovering how these foundations connect to more advanced areas of data science.
