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Bharath Prasad
Bharath Prasad

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Descriptive Statistics in Data Science: Your First Step in Analysis

If you’ve ever calculated your class average or checked a cricket player’s batting average, you’ve already worked with descriptive statistics—without calling it that.

Raw numbers alone can be hard to process. Imagine getting 5,000 sales records from an e-commerce site. By themselves, they don’t mean much. But once you calculate the average order value, find the highest sale, and create a monthly sales chart, the data starts making sense.

What Exactly Is Descriptive Statistics?
It’s the process of organising, summarising, and presenting data so you can understand it. No predictions. No guesses. Just describing what’s there—similar to introducing a friend by name, age, and hobbies.

Why It’s Important in Data Science

Gives a quick snapshot of large datasets

Helps find data entry errors or missing information

Supports better business and technical decisions

Builds the base for deeper statistical analysis

Main Types of Descriptive Statistics

Measures of Central Tendency – Mean, Median, Mode

Measures of Dispersion – Range, Variance, Standard Deviation

Visualisation – Histograms, Bar Charts, Pie Charts, Box Plots

Descriptive vs Inferential Statistics

Descriptive = Explains what’s happening now

Inferential = Predicts what could happen later

Tools to Get Started

Python: Pandas, NumPy, Matplotlib

R: summary(), mean(), ggplot2

Excel: Pivot Tables, Charts, AVERAGE(), STDEV()

Closing Note
In any data science workflow, descriptive statistics is where you start. Before forecasting the future, you need to clearly see the present—and that’s exactly what this step gives you.

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