The Analyst

Just completed the Data Analyst Bootcamp 2026! Gained strong skills in data analysis, visualization, stats, and turning data into insights Feeling pumped and ready for new challenges! Big thanks to the instructor @Alex_TheAnalyst and everyone who supported me #DataAnalytics

SQL Tip: Use COUNT(DISTINCT customer_id) instead of COUNT(*) when you want unique customers. Small change, big insight. Who’s using this tomorrow? Reply 'SQL' for more. #SQLTips

Relatable: You finally learn SQL but your first interview question is still “Tell me about a time you used VLOOKUP” 😭 Nigerian data hiring is wild sometimes. Master the basics before chasing advanced tools. Who can relate? #DataAnalysis #SQLTips

Alot of people have been Replying "ROADMAP" to my posts and I haven't been able to reply So I took my time to post so everyone can see it Save and bookmark this thread 🧵 Repost so your friends can see it too Thank you 😊👍

Beginner Data Analysis Roadmap (Nigerian Edition) - Thread Part 1: Month 1 → Master Excel (VLOOKUP, PivotTables, Power Query) Part 2: Month 2 → SQL (Joins, GROUP BY) ... Full roadmap in thread. Save this! Reply 'ROADMAP' #DataAnalysis #CareerAdvice

Remember: Consistency beats intensity. Study for 1–2 hours every day, build projects every week, and keep improving. Six months from now, you'll be glad you started today. Save this roadmap for later. 📌 #DataAnalysis #Excel #SQL #PowerBI #CareerAdvice #TechNigeria

Month 4: Build Your Portfolio Create at least 5 projects and publish them. Include:• Problem statement• Dataset• Dashboard• Key insights• Business recommendations Quality beats quantity. Start applying for internships, freelance jobs, and entry-level roles.

Month 3: Learn Power BI Master:✅ Power Query✅ Data Modeling✅ DAX Basics✅ Interactive Dashboards✅ Slicers & Filters Turn raw data into insights anyone can understand. Recreate dashboards you see online. This is one of the fastest ways to improve your skill

Month 2: Learn SQL Focus on:• SELECT• WHERE• ORDER BY• GROUP BY• HAVING• JOINS• Aggregate Functions Don't just watch tutorials—write queries every day. Build mini projects. Examples:• Sales analysis• Customer segmentation• Revenue reports• Inventory tracking

Month 1: Master Excel Learn: ✅ Data Cleaning ✅ VLOOKUP/XLOOKUP ✅ IF, SUMIF, COUNTIF ✅ PivotTables ✅ Charts ✅ Power Query Goal: Become comfortable cleaning and analyzing datasets. Ideas: • Sales records • Bank transactions • School results • Jumia product data.

Here are good options: Option 1 (Best for beginners): 🔗 github.com/microsoft/powe… Option 2 (Microsoft Sample Reports): 🔗 learn.microsoft.com/power-bi/creat…

Free resource for my people: This Power BI dashboard template for sales tracking is gold for beginners. No need to start from scratch. Just connect your Excel file and go. Link in comments. Who’s building their first dashboard this week? 💪 #PowerBI #DataAnalysis

That's it! These simple steps usually take me less than 10 minutes and save hours of troubleshooting later. 📌 Save this thread for your next Excel project. Reply "EXCEL" if you want my complete Excel Data Cleaning Checklist. #ExcelTips #DataAnalysis #Excel #DataCleaning

Thread: How I clean raw sales data in Excel in under 10 minutes (Beginner-friendly) 1/ Remove duplicates → Data tab 2/ Text to Columns for messy phone numbers 3/ Use TRIM() + PROPER() for names (continue in thread) Last step: Conditional Formatting to spot outliers. Save this.

6. Spot unusual values Use Conditional Formatting to highlight outliers or suspicious numbers instantly. This is one of the fastest ways to catch data entry errors.

5. Standardize dates Make sure every date follows the same format. Mixed date formats can break Pivot Tables, formulas, and Power BI reports.

4. Check for blanks Press Ctrl + G → Special → Blanks to quickly find empty cells that may cause problems later. Fill or remove them before analysis.

3. Clean customer names Use: =TRIM(PROPER(A2)) ✅ TRIM() removes extra spaces. ✅ PROPER() capitalizes names correctly. Example: jOHN doe → John Doe

2. Fix messy phone numbers Use Text to Columns (Data → Text to Columns) to separate or standardize inconsistent phone number formats. It makes your data much easier to sort and filter.

1. Remove Duplicates Go to Data → Remove Duplicates. This removes repeated records that can inflate your totals and lead to incorrect insights.

Raw sales data is rarely analysis-ready. Before creating charts or dashboards, I spend a few minutes cleaning the data. Here's my simple workflow. 👇

Thank you

Python for beginners: Pandas one-liner to load and preview your Excel file: import pandas as pd df = pd.read_excel('sales.xlsx') df.head() Copy, paste, run. That’s it. Reply 'PYTHON' if you find this useful. #Python #DataAnalysis

You don’t need a Data Science degree to start earning from data skills in Nigeria. Many are making extra income with Excel automation and basic dashboards for small businesses. Start small. Stay consistent. #DataCareer