How to Clean Data and Perform Calculations in Power Query: Project Based Approach

📊 Data Cleaning with Power Query: Transform Messy FIFA 21 Data into Analysis-Ready Insights! In this step-by-step tutorial, learn how to clean and transform raw FIFA 21 player data using Power Query in Excel. We’ll tackle messy data issues like: 🔹 Special characters in names, values, wages, and release clauses 🔹 Incorrect data types (text vs. numbers) in height, weight, and contract dates 🔹 Splitting columns (positions, contract dates, birthdates) for better analysis 🔹 Handling null/missing values and standardizing inconsistent entries 🔹 Removing duplicates and filtering irrelevant data 🔹 Extracting key details (year, month, day from dates) 🔹 Creating calculated columns (player age, total sales, discounts, profits) By the end, you'll have a clean, structured dataset ready for advanced analytics, dashboards, and reporting! 💡 Perfect for: ✅ Data Analysts | ✅ Excel Users | ✅ Football/Soccer Fans | ✅ Beginners in Power Query 📌 Timestamps: 00:00 – Intro & importing messy FIFA 21 data 02:50 – Fixing player names & special characters 06:15 – Cleaning currency columns (value, wage, release clause) 12:30 – Pro tip: Splitting contract dates vs. replacing delimiters 18:00 – Correcting height/weight data types 22:45 – Handling positions (splitting into multiple columns) 27:10 – Removing duplicates & filtering nulls 33:25 – Extracting dates (year/month/day) & calculating player age 38:50 – Advanced transformations (custom columns, conditional logic) 🔔 Subscribe for more Excel & Power BI tutorials! 👍 Like if you found this helpful! 💬 Comment with your data cleaning challenges! Practice Dataset: https://drive.google.com/drive/folders/1_HxovL_4BeV_ydfApwFpbb4k3m6FUBww?usp=sharing #PowerQuery #DataCleaning #Excel #FIFA21 #DataAnalysis #LearnExcel