Now, let's move to the next topic of data analytics roadmap:

Statistics Basics for Data Analysts ✅

Why Statistics Matters
- Explain trends
- Compare performance
- Avoid wrong conclusions

Descriptive Statistics
- Mean: Average value. Example: Average monthly sales ₹45,000.
- Median: Middle value. Handles outliers better than mean. Example: Typical salary in a team.
- Mode: Most frequent value. Example: Most sold product.

Spread of Data
- Range: Max minus min.
- Variance: Spread from the mean.
- Standard Deviation: How far values move from average. Low value means stable data.
Example: Avg sales ₹10,000. Std dev ₹500 means stable. Std dev ₹5,000 means volatile.

Percentages and Ratios
- Growth Rate: (Current - Previous) / Previous
- Conversion Rate: Leads to customers.

Correlation
- Relationship between two variables. Range: -1 to +1.
- Positive: Move together. Negative: Move opposite.
Example: Ad spend vs sales correlation 0.8.

Outliers
- Extreme values. Skew averages. Identify using sorting or box plots.

Sampling
- Small part of data. Saves time and cost.
- Full data often large. Samples give direction.

Common Mistakes
- Trusting averages only.
- Ignoring outliers.
- Confusing correlation with causation.

Mini Task
Take any sales data. Calculate mean, median, std dev. Check for outliers.

Statistics Resources: https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O

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