โป๏ธ Tuesday Myth & Fact
โ Myth: โSorting waste is too much stress for Nigerians.โ
โ Fact: Simple waste separation makes recycling & biogas production easier, cheaper, and more effective.
Small habits can create a cleaner Nigeria
#SustainableNigeria#WastureSolutions#Recycling
I earned my "Model data with Power BI" trophy! Iโm so proud to be celebrating this achievement and hope this inspires you to start your own @MicrosoftLearn journey!
https://t.co/Y4ErKdPCAM #MSLearnTrophy
I earned my "Create visual calculations in Power BI Desktop" badge! Iโm so proud to be celebrating this achievement and hope this inspires you to start your own @MicrosoftLearn journey!
https://t.co/Vc5CCzwm0q #MSLearnBadge
I earned my "Use DAX time intelligence functions in semantic models" badge! Iโm so proud to be celebrating this achievement and hope this inspires you to start your own @MicrosoftLearn journey!
https://t.co/Urz7RD09NJ #MSLearnBadge
I earned my "Modify DAX filter context in semantic models" badge! Iโm so proud to be celebrating this achievement and hope this inspires you to start your own @MicrosoftLearn journey!
https://t.co/jVVnZVjHmd #MSLearnBadge
This Ramadan, Wasture Solutions shares daily insights on waste management and waste-to-energy conversion.
Transforming waste into sustainable power; one fact at a time.
Follow us for daily updates. #Ramadan#Sustainability#WasteToEnergy#wastemanagement
FREE Introduction to Data Analytics folder.
Contains:
- Data Analytics Starter Kit
- 7+ hours of video lectures
- PDF and .pptx class files
- 1 SQL project and solution
- 1 Business Intelligence project
- 1 complete Power BI dashboard
- Steps to Take
- Data Analytics WhatsApp Group
If youโre interested, like and retweet then comment interested and Iโd share the link with you
๐๐ผ๐ผ๐ฑ ๐บ๐ผ๐ฟ๐ป๐ถ๐ป๐ด ๐ด๐๐๐.
If youโre starting out as a data analyst, before you hop on any tools, you need to understand the data analysis lifecycle.
This is the framework every analyst uses, whether they realize it or not.
Let me break it down. ๐งต
๐ญ. ๐จ๐ป๐ฑ๐ฒ๐ฟ๐๐๐ฎ๐ป๐ฑ๐ถ๐ป๐ด ๐๐ต๐ฒ ๐๐๐๐ถ๐ป๐ฒ๐๐ ๐ฃ๐ฟ๐ผ๐ฏ๐น๐ฒ๐บ (๐๐๐ธ)
This is where most beginners mess up.
They jump straight into tools without understanding what problem theyโre solving.
Ask:
โ What question are we trying to answer?
โ Who needs this information?
โ What decision will this analysis drive?
If you donโt understand the problem, your analysis is useless.
๐ฎ. ๐๐ฎ๐๐ฎ ๐๐ผ๐น๐น๐ฒ๐ฐ๐๐ถ๐ผ๐ป
Now you know what you need. Go get it.
Where is the data?
โ Database? Export it.
โ Excel file? Import it.
โ API? Pull it.
โ Manual entry? Document it.
You canโt analyze what you donโt have. Collect the right data from the right sources.
๐ฏ. ๐๐ฎ๐๐ฎ ๐๐น๐ฒ๐ฎ๐ป๐ถ๐ป๐ด & ๐ง๐ฟ๐ฎ๐ป๐๐ณ๐ผ๐ฟ๐บ๐ฎ๐๐ถ๐ผ๐ป
Real data is messy. Always.
Missing values. Duplicates. Wrong formats. Inconsistent entries.
Clean it:
โ Handle nulls
โ Remove duplicates
โ Fix data types
โ Standardize formats
This step takes 80% of your time. Accept it.
๐ฐ. ๐๐ฎ๐๐ฎ ๐๐ ๐ฝ๐น๐ผ๐ฟ๐ฎ๐๐ถ๐ผ๐ป
Now you start asking questions.
What patterns do you see?
โ Trends over time?
โ Outliers?
โ Correlations?
โ Unexpected values?
This is where curiosity matters more than technical skills.
Explore. Dig. Ask โwhy?โ
๐ฑ. ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ / ๐ ๐ผ๐ฑ๐ฒ๐น๐ถ๐ป๐ด
Apply your methods.
Descriptive analysis - what happened?
Diagnostic analysis - why did it happen?
Predictive analysis - what will happen?
Prescriptive analysis - what should we do?
Use the right technique for the question youโre answering.
๐ฒ. ๐๐ป๐๐ฒ๐ฟ๐ฝ๐ฟ๐ฒ๐๐ฎ๐๐ถ๐ผ๐ป & ๐ฃ๐ฟ๐ฒ๐๐ฒ๐ป๐๐ฎ๐๐ถ๐ผ๐ป
You found insights. Now make them understandable.
Nobody cares about your SQL query or your pivot table.
They care about:
โ What does this mean for the business?
โ What should we do about it?
โ Whatโs the impact?
Visualize it. Tell a story. Make it actionable.
๐ณ. ๐๐บ๐ฝ๐น๐ฒ๐บ๐ฒ๐ป๐๐ฎ๐๐ถ๐ผ๐ป
Your analysis drives a decision. The decision leads to action.
Did it work?
โ Monitor the results
โ Track the metrics
โ Measure the impact
If it didnโt work, cycle back. Refine. Try again.
๐๐ฒ๐ฟ๐ฒโ๐ ๐๐ต๐ฒ ๐ฝ๐ฎ๐ฟ๐ ๐ป๐ผ๐ฏ๐ผ๐ฑ๐ ๐๐ฒ๐น๐น๐ ๐๐ผ๐:
This isnโt a straight line.
You donโt go 1 โ 2 โ 3 โ 4 โ 5 โ 6 โ 7 and youโre done.
You cycle back and forth.
During exploration, you find data issues โ back to cleaning.
During analysis, you realize you need more data โ back to collection.
During presentation, stakeholders ask new questions โ back to analysis.
Thatโs normal. Thatโs how it works.
๐ช๐ต๐ ๐๐ต๐ถ๐ ๐บ๐ฎ๐๐๐ฒ๐ฟ๐:
Beginners think data analysis is about tools.
โShould I learn Excel or Python first?โ
โWhich BI tool is best?โ
Wrong question.
The lifecycle is the same whether you use Excel, Python, Power BI, or Tableau.
Master the process first. Tools are just ways to execute it.
Understand the lifecycle. Follow the process. Get results.
Thatโs data analysis.
#DataAnalysis #DataEngineering #DataLifecycle #BuildingInPublic #Datafam
๐2026 https://t.co/5xo7LSIrN4 New Year Airdrop๐
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NPFL Update โฝ๏ธ
Akwa United secures a narrow 1-0 victory over Nasarawa United in a thrilling encounter! ๐ฅ๐
Paul Acquah 34min goal was the difference
#NPFL#AkwaUnited#NasarawaUnited