Learning Python for data analysis can feel overwhelming 😅. But here's the truth: watching tutorials alone won't make you a better coder. Here's is actually how to master it🧵

Tips for Balancing Work, Learning, and Personal Growth as a Data Analysis 1. Set Clear Learning Goals Don’t try to learn everything at the same time. Identify what truly moves your career forward. Pick one skill per month, maybe SQL basics, DAX improvement, or Power BI...🧵

I made a video on this on my TikTok page. When you get a dataset from Open source without a business problem, here is what you should do 👇🧵

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SQL Question: What does this query return for a NULL score?

A lot of analysts use JOINs without really thinking about the logic behind them. But the difference between INNER JOIN and LEFT JOIN can completely change your results. ✔️INNER JOIN keeps only the rows that match in both tables. ✔️LEFT JOIN keeps all rows from the left table

See if you can nail these four SQL questions From EASY to INTERMEDIATE: 1: How do you get the average order value per customer? A) SELECT customer_id, AVG(amount) FROM orders B) SELECT customer_id, AVG(amount) FROM orders GROUP BY customer_id C) SELECT AVG(amount) FROM

· Uses a CASE statement to handle customers with zero payments gracefully (avoids divide-by-zero errors!) Key takeaway: Always protect your aggregates! The CASE WHEN count(...) = 0 THEN 1 ensures safe averaging even when no payments exist.

Here’s a clean SQL query that: · Joins customer and payment tables with a LEFT OUTER JOIN (keeps all customers, even those who never paid) · Calculates: · Total payment amount per customer · Number of payments made · Average payment per transaction

Ever wondered how to analyze customer payment behavior while avoiding division-by-zero errors? #SQL #SQLTips

Real-world use cases: · User engagement metrics · Conditional analytics reporting · Performance-optimized aggregations Pro Tip: While correlated subqueries can be efficient for small-medium datasets, for large tables consider testing against JOIN + GROUP BY alternatives!

Why this pattern rocks: 1. Performance: Avoids unnecessary joins for inactive users 2. Readability: Clear business logic (active vs inactive handling) 3. Flexibility: Easy to modify thresholds or add conditions

What's happening here? ✅CASE Expression: Dynamically decides whether to count rentals ✅Subquery in SELECT: Correlated subquery counts rentals per customer ✅Active Flag Logic: Inactive customers (active=0) show 0 rentals instantly

SQL Deep Dive: Conditional Counts with CASE & Subqueries! Check out this MySQL query that calculates customer rental activity with a conditional twist: #SQL #Database

QUERY 2: The Horizontal Pivot 👉 Returns: One row WITH columns 📊Perfect for: Dashboards, summary reports Key Insight: Both answer"How many rentals per month?" but structure matters! · Group By = Dynamic (adds months automatically) · Pivot = Static (you define each column)

QUERY 1: The Vertical Approach 👉 Returns: One row PER month 📊Perfect for: Charts, time series analysis

SQL Pro-Tip: Same Data, Different Dimensions! Ever noticed how SQL can give you the same results in completely different formats? Check out these two queries: #SQL #DataAnalytics

December is here🎄!

LEFT JOIN or RIGHT JOIN? 🤔 Both can give identical outputs when structured correctly. Both queries ensure all customers are included, even those with no payments. The choice between LEFT and RIGHT JOIN often comes down to readability and table relationship emphasis. #SQL

Pro Tip: If you find yourself using RIGHT JOIN, try flipping the table order and using LEFT JOIN instead - your future self (and teammates) will thank you! The beauty? LEFT JOIN ensures we don't miss films that might have zero inventory copies! 🎬

Why LEFT OUTER JOIN dominates over RIGHT OUTER JOIN: 1️⃣ Readability - Follows natural left-to-right thinking 2️⃣ Query Structure - Maintains your main table as the anchor 3️⃣Mental Model - Easier to think 4️⃣Consistency - Most SQL developers default to LEFT JOIN