PRIME

The best way to end the year 🥹😍 Didn’t deserve it I earned it

@Japa_Queen Congratulations Divine

A gig from Utiva? Bring 🤭 I’m accepting “Congratulations”

Sometimes I go back to basics to understand something technical.

@RuthChika12 😌1280 * 720 >>> 1920 * 1080😌 Gives more space for those names hiding and table.

@Prime_Analysis 1280 * 720 the default power bi canvas size

Just finished a guided project on Sales Analysis project using SQL Server & Power BI. I extracted and cleansed data from a real database, then built 3 interactive dashboards to answer actual business questions. Full project on GitHub 👇 🔗 github.com/Ruth-Chika/sal… #120DaysOfDataWithTina #SQL #PowerBI #BuildInPublic #dataanalysislockedin

@JA_Olaoye On point sir Been on it low-key

What you can slowly add to your Data Analytics skills. Business Automation, Data Engineering. Don’t thank me, act instead.

@dekhane_mukul Poco loco

Don't force children to take science if they interested in Arts Issued in public interest!! 😂😄🤣 #Wisdom

Bi Developers, on avg, how long will this take in Power BI or Tableau? I know Excel can’t do it so excel people please shit. Note: this is a UI design by @Agunbiadesina1 not an actual analysis.

This DAX Measure let’s you send Emails Directly from Power BI Full video on YouTube ✌🏻 #powerbi #report #dax

@ObohX @iam_daniiell 😂😂😂 Yakubu you can always manage No fear

@Prime_Analysis @iam_daniiell This year I say make I dey mind my business, as I see am I pass 😂😂 Make una no put me for talk

😂😂😂 Omo na joke ooo, excel can actually do it My bet on @ObohX and @iam_daniiell

@_VictorUgwu @Agunbiadesina1 Na me talk am Oya do your worst 😂

@Prime_Analysis @Agunbiadesina1 Who said Excel people can't recreate it

@dhisaniya @Agunbiadesina1 Hmmm 3-5 days It’s a 6 page analysis ooo

@Prime_Analysis @Agunbiadesina1 It takes 3 to 5 coz thinking of what data to show takes alot of time for me.

@Agunbiadesina1 Beautiful work How long does it take you?

I challenged myself to design a modern, clean, and data-driven eCommerce dashboard UI

@pengs_love Idolo stop scrolling

Stop scrolling Get to learning

You think that you need to learn all tools for you to become a data analyst or job ready? Guess what, you can be a data analyst and job ready by first learning data analytics concepts, context and Excel. Many have limit data analytics to Tools and dashboard.

I don’t get why this sounds funny. In the right context, you become job ready as a data analyst within 3 months.

Data Analytics mistakes beginners should avoid: 1. Jumping Straight to Visuals - Skipping Data Cleaning (EDA) - Leads to incorrect charts - Clean and explore data first - Understand the "shape" of your data 2. Relying Solely on Excel - Limited with large datasets - Hard to automate complex tasks - Learn SQL for data extraction - Try to use Python/R for advanced analysis 3. Overcomplicating Visualizations - Too many colors and chart types - Confuses the end-user - Keep it simple and clean - Use the right chart for the right data 4. Ignoring the "Why" (Business Context) - Reporting numbers without meaning - Analysis doesn't solve a problem - Understand business goals first - Focus on actionable insights 5. Poor SQL Habits - Using SELECT * on huge tables - Writing unreadable, messy queries - Use aliases and formatting - Filter data early with WHERE 6. Missing Outliers and Distributions - Only looking at the "Average" (Mean) - Outliers can skew your results - Check median and standard deviation - Visualize distributions with histograms 7. No Documentation or Comments - Hard to reproduce your work - You’ll forget your logic in a month - Document your data sources - Comment your code and SQL scripts 8. Correlation vs. Causation - Assuming $A$ caused $B$ just because they moved together - Leads to false business advice - Look for underlying factors - Use A/B testing where possible 9. Not Validating Results - Trusting the output blindly - Logic errors in formulas/queries - Cross-check totals with raw data - Peer-review your findings 10. Poor Communication Skills - Great analysis, but poor presentation - Getting too technical with stakeholders - Tell a story with your data - Focus on the "So What?" for the audience