Tools: How I Started Doing Data Analysis with MS Excel

1. Understanding Data in Excel (The Foundation) ## 2. Cleaning Data (Because Real Data Is Always Messy) ## Common cleaning tasks in Excel: ## 3. Sorting and Filtering (Finding Meaning Fast) ## 4. Using Simple Formulas (Excel Starts Thinking for You) ## 5. Conditional Formatting (Let Excel Highlight the Story) ## 6. Pivot Tables (Summary Without Stress) ## 7. Charts and Visuals (Making Data Human) ## Final Thoughts: Excel Is the Gateway Drug I didn’t wake up one day and say “Yeah, today I become a data analyst.” It started with Excel. Just rows. Columns. Confusion. And a LOT of scrolling. But somewhere between cleaning messy data and making my first chart, I realized something wild:Excel is basically data analysis in disguise. If you’re a beginner and Excel feels scary, relax you’re exactly where you’re supposed to be. In this article, I’ll walk you through how MS Excel can be used for basic data analysis, using simple language, real-life vibes, and practical examples. Before analysis, there must be data. In Excel, data usually lives in: Rows → individual records (one person, one sale, one day) Columns → variables (name, age, salary, date, etc.) Think of Excel like a table in real life: Each row is one story Each column is one detail about that story Row 2 = Bongo’s details Column C = everyone’s salary If your data has clear headers and no empty random rows, you’re already winning. Nobody talks about this part enough. Mixed uppercase and lowercase Sometimes straight-up wrong Before analysis, we clean. Removing extra spaces using TRIM() Making text consistent using UPPER(), LOWER(), or PROPER() Fixing date and number formats " bongo lala " → "Bongo Lala" Story moment: The first time I cleaned data, I thought I was doing something wrong because the numbers suddenly made sense. Turns out… that’s the point. Imagine having 500 rows of data and trying to “just look” for answers. That’s where Sort and Filter save your life. Arrange salaries from highest to lowest Order dates from oldest to newest See only Sales department View employees above age 30 Focus on specific categories 💡 Beginner win: If you can filter data, you can already answer real business questions. This is where Excel stops being a table and starts being smart. Basic formulas used in data analysis: COUNT() → number of entries MAX() / MIN() → highest & lowest values Example questions Excel can answer: What is the total salary paid? What is the average age? Data doesn’t always speak. Conditional Formatting lets you: Highlight high or low values Color-code performance Spot patterns instantly Salaries above 100,000 → green Pivot Tables sound scary. Think of a Pivot Table as: A summary button for large data With Pivot Tables, you can: Count employees per department Compare categories easily And the best part? No formulas needed. But visuals? They hit different. Excel charts help you: Explain data to other humans Common beginner charts: Pro tip: If someone understands your chart in 5 seconds, you did it right. Most people think data analysis starts with Python, SQL, or Power BI. But for many of us? It starts with Excel. How data is structured And once that clicks… everything else becomes easier. So if you’re learning Excel right now — keep going. You’re not just learning a tool. You’re learning how to think with data. If this helped you, feel free to share it or drop your Excel learning story. We’re all just one spreadsheet away from greatness. Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse - Rows → individual records (one person, one sale, one day) - Columns → variables (name, age, salary, date, etc.) - Each row is one story - Each column is one detail about that story - Row 2 = Bongo’s details - Column C = everyone’s salary - Has extra spaces - Mixed uppercase and lowercase - Sometimes straight-up wrong - Removing extra spaces using TRIM() - Making text consistent using UPPER(), LOWER(), or PROPER() - Removing duplicates - Fixing date and number formats - Arrange salaries from highest to lowest - Order dates from oldest to newest - Rank scores - See only Sales department - View employees above age 30 - Focus on specific categories - SUM() → total values - AVERAGE() → mean - COUNT() → number of entries - MAX() / MIN() → highest & lowest values - What is the total salary paid? - What is the average age? - Who earns the most? - Highlight high or low values - Color-code performance - Spot patterns instantly - Salaries above 100,000 → green - Low scores → red - Why this matters*: Your eyes understand colors faster than numbers. - Count employees per department - Sum sales per month - Compare categories easily - Compare values - See trends over time - Explain data to other humans - Column charts - Line charts - How data is structured - How to ask questions - How to find answers