PRIME

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PRIME

@Prime_Analysis

Financial Analyst || Operations Analyst

Nigeria Beigetreten Eylül 2017
1.3K Folgt1.2K Follower
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PRIME
PRIME@Prime_Analysis·
The best way to end the year 🥹😍 Didn’t deserve it I earned it
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Divine Chukwuemeka
Divine Chukwuemeka@Japa_Queen·
A gig from Utiva? Bring 🤭 I’m accepting “Congratulations”
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PRIME
PRIME@Prime_Analysis·
@RuthChika12 😌1280 * 720 >>> 1920 * 1080😌 Gives more space for those names hiding and table.
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PRIME
PRIME@Prime_Analysis·
@JA_Olaoye On point sir Been on it low-key
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J.A Olaoye
J.A Olaoye@JA_Olaoye·
What you can slowly add to your Data Analytics skills. Business Automation, Data Engineering. Don’t thank me, act instead.
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PRIME
PRIME@Prime_Analysis·
Sometimes I go back to basics to understand something technical.
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Mukul Dekhane
Mukul Dekhane@dekhane_mukul·
Don't force children to take science if they interested in Arts Issued in public interest!! 😂😄🤣 #Wisdom
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PRIME
PRIME@Prime_Analysis·
@Agunbiadesina1 Beautiful work How long does it take you?
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Demilade 𝕏 🇦🇸
Demilade 𝕏 🇦🇸@Agunbiadesina1·
I challenged myself to design a modern, clean, and data-driven eCommerce dashboard UI
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PRIME
PRIME@Prime_Analysis·
Stop scrolling Get to learning
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PRIME retweetet
Splendor of SQL 🇬🇧💖
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
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