Descriptive Statistics

Course

Learn how to describe data in terms of data types, measures of center, measures of spread, shape, and outliers. These essential skills in descriptive statistics provide the foundation for more advanced statistical techniques that are used for data science, data analysis, and machine learning.

Beginner
2 weeks
Last Updated January 14, 2025

Skills you'll learn:

Basic descriptive statisticsOutlier analysis

Prerequisites:

Basic arithmeticElementary algebra

Beginner

2 weeks

Last Updated January 14, 2025

Skills you'll learn:

Basic descriptive statistics • Outlier analysis

Prerequisites:

Basic arithmetic • Elementary algebra

Course Lessons

Lesson 1

Introduction

In this lesson, we kick off the course with an introduction to data and descriptive statistics.

Lesson 2

Data Types

In this lesson, we establish key distinctions between different types of data, including quantitative, categorical, ordinal, nominal, continuous, and discrete data types.

Lesson 3

Measures of Center

In this lesson, we get into the calculations and use cases for three popular measures of center: mean, median, and mode.

Lesson 4

Notation

In this lesson, we demystify the mathematical notation used for random variables, observed values, and aggregations.

Lesson 5

Measures of Spread

In this lesson, we get into the calculations and use cases for several popular measures of spread, including five-number summaries, standard deviation, and variance.

Lesson 6

Shape and Outliers

In this lesson, we cover two additional forms of descriptive statistics for data distributions: shape and outliers.

Lesson 7 • Project

Describe Health and Sleep Quality Data

Describe data related to health and sleep quality. Using the software of your choice to calculate descriptive statistics about a dataset, you’ll report your findings in a slide deck presentation.

Taught By The Best

Josh Bernhard

Staff Data Scientist

Josh has been sharing his passion for data for over a decade. He's used data science for work ranging from cancer research to process automation. He recently has found a passion for solving data science problems within marketplace companies.

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Demonstrate proficiency with practical projects

Projects are based on real-world scenarios and challenges, allowing you to apply the skills you learn to practical situations, while giving you real hands-on experience.

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Reviewers provide timely and constructive feedback on your project submissions, highlighting areas of improvement and offering practical tips to enhance your work.