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
- 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
WHERE6. 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
- Youโll 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
Double Tap โฅ๏ธ For More