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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