Arinze || Data

5 Ways To Become A Badass Data Analyst In Less Than 100 Days From The Comfort Of Your Bedroom... if you read to the end, you will surely unlock your potential to become a recruiter-wanting data analyst Follow @data_chief

@AskMichaelTaiwo Absolutely Sleep consolidates what you learn and reading expands what you know together they're the most underrated productivity stack on the planet. No app, no hack, no supplement comes close. Free, accessible, and powerful every single time

Sleeping and reading books are two acts that never fail to rewire the brain. Anytime. Any day. They always work the magic.

@KenGuient This right here! Simplicity is the most underrated strategy. People spend months building the "perfect" funnel, logo, and website but forget to just show up and talk to people. Pick your lane, stay consistent, and let compounding do the work. Simple really is sustainable.

If you’re starting from scratch: Don’t overcomplicate it. Pick something you want to be known for. Post about it. Then talk to people like a human. That's it. Simple is sustainable.

@jonbrosio Most people overcomplicate it. The formula has always been simple solve a real problem, put it in front of the right people, and don't be afraid to ask for the sale. The hardest part isn't the skill, it's believing your skill is worth paying for

You don't need a big audience to make $10k a month. You need: • A skill • An offer • A way to get leads • A way to close them That's it.

@Mr_Akinlabi @NHSNSS @NHSResearchScot @NHSScotland @NHSuk @HSCScotland @P_H_S_Official @VHSComms @online_his his is clean work. A Star Schema with dual fact tables is the backbone of any serious Power BI dashboard. Getting those relationships right ensures seamless interactivity across slicers and filters. This is the kind of data modeling that separates good dashboards from great ones!

This is the Power Pivot Model, a Star Schema structure with two central fact tables surrounded by five dimension tables connected through relationships. This foundation makes the entire dashboard interactive, ensuring every slicer and filter updates both datasets simultaneously.

Pep Guardiola does it AGAIN! Man City crush Arsenal 2-0 at Wembley to lift the Carabao Cup! Nico O'Reilly with a stunning second-half brace what a night for the Citizens! 9 League Cups and counting. Pep simply NEVER misses a final. #MCFC #CarabaoCupFinal #PepGuardiola"

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Honest question for my data community 👇 Where are you on your data journey right now? → Complete beginner, just started exploring B → Learning the tools but haven't built anything yet → Built a project or two but no job yet D → Already working in data

Python for Data → Kaggle.com/learn (completely free) Practice Datasets → Kaggle.com/datasets Full Data Courses → Google Data Analytics on Coursera (audit for free) You have no excuse not to start. Bookmark this. Share it with one person who needs it

Free resources that will teach you data skills better than a $500 course 👇 Excel → ExcelJet.net (free, fast, practical) SQL → SQLZoo.net (interactive, beginner friendly) Power BI → Microsoft Learn (free official tutorials)

Now you know the door. Will you walk through it? Drop "DATA" in the comments if you're starting your journey today 👇

→ builds dashboards & reports → Data Entry Specialist → organizes and structures raw data → Junior Data Scientist → builds basic prediction models The barrier isn't talent. It's people not knowing the door exists.

Data jobs in Africa are growing faster than the talent pool. Translation: companies are desperate and salaries are rising. Entry level data roles you can aim for right now 👇 → Data Analyst → finds insights from existing data → Business Intelligence Analyst

Garbage in. Garbage out. Always. Save this for when you start your first dataset

→ A date column that says "12/03/26" in some rows and "March 12" in others → A salary column with "N50,000" and "50000" mixed together → Duplicate rows of the same customer Dirty data gives you wrong answers even when your analysis is correct.

Everyone keeps saying "clean your data." But nobody explains what dirty data actually looks like. Here are 5 examples 👇 → Same name spelled two ways: "Lagos" and "lagos" → Empty cells where numbers should be

Week 4 → Analyze a free dataset from Kaggle and post your findings That's one month. After that you have a portfolio. After a portfolio you have leverage. Most people are still "preparing to start" 6 months later. Don't be most people.

If I was starting my data journey today from zero, here's exactly what I'd do Week 1 → Learn Excel basics (sort, filter, pivot tables) Week 2 → Learn basic SQL (SELECT, WHERE, GROUP BY) Week 3 → Build one simple dashboard in Power BI or Tableau

→ NotebookLM = analyze documents with plain English You can land your first data role before writing a single line of code. Stop waiting until you're "ready." Start today. RT if this helped someone you know

ou need to know how to code to work with data. This is the biggest lie keeping beginners stuck. Here's the truth → Excel & Google Sheets = data analysis, no code needed → ChatGPT = writes SQL queries for you in seconds → Power BI & Tableau = drag and drop dashboards

→ Your job as a data person is to find the story hiding in the numbers. → That story helps businesses make smarter decisions. → Better decisions = more money saved or made. That's it. You're not a mathematician. You're a translator. Follow me for more breakdowns like this

Nobody taught you this in school. But data is the most valuable skill you can learn in 2026. Here's what data actually means for a beginner → Data is just information. Numbers, names, dates, clicks. → Companies collect it to understand what's happening in their business.