Just completed my second-semester capstone project for @AltSchoolAfrica! Built a SQL analysis of vehicle incident reports to identify patterns in theft, abandonment, and recovery for public safety stakeholders. Full project on GitHub: github.com/feith-i/sql-vehic… #SQL #DataAnalysis

Good morning to y'all 🌞 Wish you a great weekend

Just survived a 2‑month Python dataset project Massive, messy data → cleaned it → made new columns → visualized it → documented everything. Lowkey proud of how much I leveled up. Thanks to my instructor at @AltSchoolAfrica for the chaos.

I have noticed something about most analysts on X and LinkedIn You are consistently posting: Dashboards, insights, learning journeys, project breakdowns… But still getting low engagement. And it makes you start to wonder: Why am I putting in so much effort when the results are silent echoes? What you lack is visibility support. That is why I’m building something specifically for data analysts. A space where analysts: • Support each other’s posts with real engagement. • Gain visibility through community backing. • Laugh, relax and breathe while growing. Stay close. Something huge is coming📌 Repost and quote for wider reach🌼

Me: "I'll learn SQL for 30 minutes." SQL Error: "Syntax error near..." Me: spends 2 hours finding a missing comma.

Happy New Month guys 🥳🥳 As you sip your coffee and query the day’s first dataset, remember: every great insight starts with a single SELECT. Whether you're battling schema drift, optimizing dashboards, or hunting anomalies just remember June will still be chaotic 🤪

🧹 Retail data cleaning today: 11 columns → 5 cleaned, 6 to go. ✅ Nulls, types, renamed cols ✅ Filled missing prices by item (not mean/median) For e‑commerce folks – what’s your #1 data cleaning headache? #DataCleaning #Python

Excel Power Query → Data Cleaning & Transformation Excel Pivot Tables → Data Analysis Excel Solver → Optimization & Forecasting Excel Charts → Data Visualization Excel Conditional Formatting → Insight Highlighting Excel Power Pivot → Advanced Data Modeling Excel AI → Junior Analyst Replacement

Note: This is logical order – how SQL interprets your query. Real databases may physically reorder steps for performance (indexes, filters). But knowing this logic prevents mistakes.

7️⃣ ORDER BY – Sort final results (aliases finally allowed!) 8️⃣ LIMIT / OFFSET – Return a subset of rows This is why ORDER BY can use aliases but WHERE can’t.

5️⃣ SELECT – Project expressions assign aliases 6️⃣ DISTINCT (if used) – Remove duplicate rows Aliases finally exist… but only for steps that come after SELECT.

3️⃣ GROUP BY – Group rows (aggregates computed right after) 4️⃣ HAVING – Filter groups (aggregates allowed here) WHERE = rows. HAVING = groups. Don't mix them up.

1️⃣ FROM / JOIN – Identify tables apply ON conditions 2️⃣ WHERE – Filter rows (no aliases yet – they don’t exist) Aliases in WHERE? Won’t work. Now you know why.

Here’s the actual logical processing order: FROM → WHERE → GROUP BY → HAVING → SELECT → ORDER BY → LIMIT/OFFSET ✔️ Understanding this = fewer bugs faster debugging.

🐍 Python Quiz: What is the output of [1,2,3,4][1:3]? A) [2,3] B) [2,3,4] C) [1,2,3] D) [3,4] #Python #CodingQuiz

📊 Excel Quiz: Which formula correctly counts cells in A1:A10 containing "Yes" (case‑insensitive)? A) =COUNTIF(A1:A10,"Yes") B) =COUNTIFS(A1:A10,"Yes") C) =COUNT(A1:A10,"Yes") D) =IF(A1:A10="Yes",COUNT()) #Excel #ExcelQuiz

🧠 SQL Quiz: Which clause filters rows before grouping in an aggregate query? A) WHERE B) HAVING C) GROUP BY D) ORDER BY #SQL #LearnSQL

Combination of Critical thinking Power pivot Dax Pivot tables Ui Design skills… Resulted in to this …📈👇👇