When I started learning Data Science, I thought the first thing I would learn was Python. I expected to spend my first week writing code and learning about machine learning.
Instead, our instructor introduced us to Excel.
At first, I didn't understand why we were starting with a spreadsheet. I had always thought Excel was mainly used to enter numbers and create simple tables. But after working with an employee dataset in class, I realized there was much more to it.
As we practiced sorting data, filtering records, and using formulas like SUM(), AVERAGE(), and COUNT(), I began to see why Excel is still an important tool for data analysts. By the end of the week, I understood that before you can analyze data with programming languages, you first need to know how to organize and understand the data itself.
Looking back, starting with Excel made a lot more sense than I expected. It gave me a strong foundation and made me feel more confident about the next stages of my Data Science journey.
Real-World Uses of Excel
Excel is widely used across industries for handling and analyzing data. Some of the most common uses include:
- Business Analysis - Tracking sales and identifying trend
- Accounting and Budgeting - Managing Expenses, Profits and Financial reports
- Marketing Analysis - Measuring campaigns performance and customer behavior
- Data Entry and Management - organizing large datasets efficiently
Businesses rely on Excel because it helps turn raw data into meaningful insights for decision making.
Key Excel Features I Learned
In my first week, I explored several important Excel Features that help with data organization and analysis:
- Excel Interface Overview - I first explored how Excel is organized, including Ribbon, Worksheets, Cell, Row, Columns, and formula bar. this helped me understand how to navigate the tool before working with data.
Excel interface
Excel Cell indicated in yellow
Excel Row indicated in Green A2
excel column indicated in yellow
- Data Sorting - Organizing data by numbers, Text and Dates
- Filtering - Showing only relevant data based on condition
- Data Validation - Ensuring accurate and consistent data entry
- Freeze Panes - Keeping header Visible while scrolling through large datasets.
These features make working with data much easier, faster and more structured.
Basic Excel Functions I learned
I was also introduced to some basic Excel functions used in Data Analysis.
Aggregate Functions
-SUM - Add all values in a range
Sum salary of employee =sum(E2:E870)
-AVERAGE - Calculate the mean of a dataset
Finding Average salary we use =Average(E2:E870)
-COUNT - Counts numerical entries in a dataset
Count employee with ages recorded =count(G2:G870)
Conditional Functions
-SUMIF() and SUMIFS() - Add values that meets one or more conditions
-COUNTIF() and COUNTIFS() - Counts records that match specific criteria
-AVERAGEIF() and AVERAGEIFS() - Calculate average based on selected conditions
These functions help analysts answer business questions such as total sale by product, customer count by region, or average performance across departments.
OTHER FUNCTIONS
I also learned about:
-TEXT FUNCTIONS such as LEFT(), RIGHT(), LEN(), and CONCAT()
-DATE and TIME FUNCTIONS such as TODAY(), NOW(), DAY(), MONTH(), YEAR()
These functions help analyst clean data, identify Trends, and create meaningful reports.
What this changed for me
Before this week, I thought Excel was just a tool for entering data and doing simple calculations. After working with an employee dataset and practicing different formulas, I realized how much you can learn from well-organized data. Even simple tasks like sorting, filtering, and calculating totals became much easier than doing them manually.
The biggest lesson for me wasn't just learning new formulas—it was understanding the importance of organizing data before trying to analyze it. That foundation will be useful as I continue learning Data Science.
Final Thoughts
Although this was only my first week, I now understand why many Data Science and Data Analytics courses begin with Excel. It has helped me build confidence in working with datasets and understand the basics before moving on to programming.
My next goal is to learn more advanced Excel features like Tables, XLOOKUP, and Dashboards before continuing with Python and data visualization. I'm excited to keep learning and see how these skills connect as I progress.
What was your first experience with Excel like?
If you're learning Data Analytics or already working in the field, I'd love to hear about your experience and any tips you have for someone just getting started.
Top comments (3)
@josepho_okwemba_a02e2378a - This is really good work. I haven't interacted with excel in a while, the most recallable memory is when I do my finances. I would have loved to see some examples in your post using the basic functions that you learn't. Goodstuff, keep it up.✅️
Good effort. The article is clear and well structured, but it feels AI-generated because some sections sound generic and list-based; mainly because of the very polished structure, emoji headings, generic phrases like “raw data into meaningful insights,” and the broad list-style explanations. Please make it more personal by adding a specific dataset example, explaining formulas you used in class, add screenshots and examples, and reducing the broad motivational phrases and resubmit your work!
Thank you for the feedback. I appreciate the suggestions and will revise the article to make it more detailed and personal.
I will include the specific Excel dataset used, explain the steps followed during data cleaning and analysis, add examples of the formulas applied, and include screenshots of the actual worksheets and results. I will also reduce the general explanations and focus more on my practical experience and the process followed while working on the project.
The updated version will reflect a more practical and personal approach based on the Excel work completed.