Think Like a Data Analyst

📢 𝐄𝐧𝐫𝐨𝐥𝐥 𝐍𝐨𝐰

Join our Online Data Analytics Course and start your journey to becoming a job-ready data analyst. 💻📊

✅ Learn Power BI, Excel, SQL, Python & more
✅ Work on real-time projects & case studies
✅ Get certification support & career guidance
✅ Learn at your own pace – anytime, anywhere

4 months ago | [YT] | 2

Think Like a Data Analyst

5 months ago | [YT] | 1

Think Like a Data Analyst

Are you still confused about how Power Query works in Power BI? 🤔 This visual breakdown makes it super easy.

✅ Data Sources – Excel, CSV, SQL Server, Web
✅ Transformations – Filter, Group By, Pivot/Unpivot, Add Columns
✅ Combining Queries – Append vs Merge
✅ Loading Data – Into Excel or Power BI

🚀 Whether you're just starting or want to brush up your skills, this guide is your one-stop resource!

🎥 Watch the full video and transform how you handle data.
📊 Think like a data analyst – Power up with Power Query.

watch full video - https://www.youtube.com/watch?v=LnNnf...

#PowerBI #PowerQuery #DataTransformation #BusinessIntelligence #DataCleaning #ExcelToPowerBI #ETL #DataAnalytics

5 months ago | [YT] | 1

Think Like a Data Analyst

Python tips and tricks to level up your coding. 😀


First, you can easily swap two variables without needing a temporary variable by using a, b = b, a, which makes your code cleaner. When you need to ignore certain values in loops, use _ as a throwaway variable. For example, for _ in range(5) runs the loop 5 times without needing to store the index!


Need to get the last element of a list? Just use my_list[-1] to grab it instantly. In Python 3.9+, you can merge two dictionaries in a single line using |, making dictionary handling more intuitive.


Finally, Python allows you to write one-line if statements like status = "Success" if condition else "Fail", keeping your code neat and concise. Mastering these small yet powerful techniques will help you write cleaner and more efficient Python code! 🚀✨




#PythonTips #CodingHacks #PythonTricks

1 year ago | [YT] | 1

Think Like a Data Analyst

🔍 SQL Tip: What's the Difference Between COUNT(*) and COUNT(1)? 🧮

If you're curious about how COUNT(*) differs from COUNT(1), here's a quick breakdown using emojis:

1️⃣ Basic Count

COUNT(*) ➡️ 🧮 Counts all rows in a table.
COUNT(1) ➡️ 🧮 Also counts all rows by evaluating a constant.
2️⃣ Nulls

Both COUNT(*) and COUNT(1) ➡️ ❓✅ Include rows with NULL values.
3️⃣ Columns Involved

COUNT(*) ➡️ 📦 Doesn’t check any specific column, just counts rows.
COUNT(1) ➡️ 🧩 Similarly, evaluates the constant 1 for row count.
4️⃣ Performance

⚡ Both are optimized and perform equally in most databases!
5️⃣ Practical Difference

Both are used to count rows, but COUNT(*) is more commonly seen in SQL queries. 🎯
In most scenarios, they function the same – just a difference in syntax!

1 year ago | [YT] | 1