Descriptive Statistics

Learn how statisticians organize, summarize and interpret data. This course builds the foundation required for probability, inference, regression and advanced analytics.

Beginner 4 Hours 7 Modules Certificate Eligible
Overview
Introduction to Statistics
Data Types
Central Tendency
Dispersion
Skewness & Kurtosis
Practice Lab
Final Assessment

Course Overview

Descriptive Statistics is the branch of statistics concerned with collecting, organizing, summarizing and presenting data in meaningful ways. Before building predictive models or performing hypothesis tests, analysts must first understand the structure of their data.

Learning Outcomes

  • Understand statistical thinking
  • Differentiate data types
  • Calculate averages correctly
  • Measure variability
  • Interpret distributions
  • Analyze real datasets

Module 1: Introduction to Statistics

What is Statistics?

Statistics is the science of learning from data. It involves collecting, organizing, analyzing and interpreting information in order to support better decisions.

Modern governments, hospitals, universities, banks and businesses all depend on statistical methods.

Worked Example

A school wants to understand student performance. Instead of relying on opinions, it collects exam scores, attendance rates and graduation statistics.

Real World Scenario

A retail company operates 50 stores. Management wants to know which locations perform best. Statistics helps transform raw sales data into actionable insights.

Knowledge Check

Which best describes statistics?




Module 2: Data Types

Data may be qualitative or quantitative. Quantitative data can be discrete or continuous.

Age, income and height are quantitative variables. Gender and nationality are qualitative variables.
A hospital records patient age, gender and blood pressure. Can you classify each variable?

Module 3: Measures of Central Tendency

The center of a dataset is often described using Mean, Median and Mode.

Dataset: 70, 75, 80, 85, 90 Mean = 80 Median = 80 Mode = None
A business wants to determine typical monthly sales. Should it use the mean or median?

Module 4: Measures of Dispersion

Dispersion measures variability. Common measures include Range, Variance and Standard Deviation.

Dataset A: 48,49,50,51,52 Dataset B: 10,20,50,80,90 Same center. Different spread.

Module 5: Skewness & Kurtosis

Skewness describes asymmetry while kurtosis measures tail behavior.

Income distributions are often positively skewed because a few individuals earn substantially more than the majority.

Module 6: Practice Lab

Analyze a dataset and calculate:

  • Mean
  • Median
  • Mode
  • Range

Final Assessment

Complete the final assessment to qualify for certification.

  • 25 MCQs
  • Passing Score: 50%
  • Certificate Eligible