Foundations of Statistics: Descriptive Statistics

Name: Foundations of Statistics: Descriptive Statistics
Rating: 4.8 (4 reviews)

Master the essentials of descriptive statistics to analyze, summarize, and interpret data with confidence.

Created byalphas &betas

Last updated 3/2026

English

What you'll learn

Summarize and organize data effectively using counts, frequencies, and descriptive tables to reveal patterns and trends.
Calculate and interpret measures of central tendency, relative location, and spread to understand the distribution and variability of data.
Identify and handle outliers to ensure accurate and meaningful statistical analysis.
Standardize and summarize data for easy comparison and clear communication of results.

Course content

1 section • 8 lectures • 30m total length

Overview1:31
Explore your data with descriptive statistics to summarize observations using counts, central tendency, spread, and outliers, and learn when to use numbers, graphs, and how to standardize values.
Count1:44
Count categorical responses to create a frequency table, identify the mode, and convert counts to percentages. Ensure categories are mutually exclusive and exhaustive and avoid averaging labels.
Central tendency8:00
Explore central tendency by examining the mean, median, and mode, including how outliers bias the mean and when the median better represents typical values.
Relative location3:27
Explore relative location in descriptive statistics by ordering data from high to low and applying the median, quartiles, and percentiles to divide observations into equal groups.
Spread7:21
Examine the five measures of spread: range, interquartile range, variance, standard deviation, and coefficient of variation, and understand how outliers impact them.
Outliers1:38
Explore how to handle outliers in descriptive statistics by deciding when to remove them, investigating data collection, and recognizing robust measures like the mode and median.
Standardization4:22
Standardization rescales data by converting a value into the number of standard deviations from the mean, yielding a standardized value. This enables cross-dataset and unit-free comparisons.
Summary2:40
Learn how descriptive statistics summarize data with mean, median, mode, range, variance, standard deviation, interquartile range, and coefficient of variation, and assess distribution shape and outliers.

Requirements

No prior knowledge of statistics is required.

Description

[This course contains the use of artificial intelligence.] - Voiceover

Understanding data is the first step to making informed decisions, and descriptive statistics provides the essential tools to summarize and interpret data effectively. This course, Foundations of Statistics – Descriptive Statistics, is designed for beginners, students, and professionals who want to gain a solid foundation in statistical analysis. Through step-by-step lessons, you will learn how to organize and summarize data, calculate key statistical measures, identify patterns, and detect anomalies.

You will start by learning how to count data points and create summaries that reveal trends and structures in datasets. Next, you’ll explore measures of central tendency, such as mean, median, and mode, to understand the “typical” values in your data. You’ll also learn about relative location measures, including percentiles and quartiles, which allow you to compare individual data points within a dataset.

The course covers measures of spread—range, variance, and standard deviation—to help you understand variability, as well as techniques for identifying and interpreting outliers that could skew results. You will also learn how to standardize data, enabling meaningful comparisons across different datasets.

By the end of this course, you will be able to create clear, comprehensive summaries of your data and confidently interpret the results. Whether you are preparing for advanced statistics, conducting research, or analyzing business data, these skills will provide a practical, reliable foundation for all your data analysis tasks.

Who this course is for:

Beginners with little or no prior knowledge of statistics.