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.
