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Statistics and data literacy for non-statisticians
Rating: 4.5 out of 5(2,574 ratings)
42,010 students

Statistics and data literacy for non-statisticians

Learn the key terms and analysis methods in statistics
Created byMike X Cohen
Last updated 8/2026
English
English [Auto],

What you'll learn

  • Important terms in statistics
  • Overviews of the most important statistical methods

Course content

5 sections • 18 lectures • 1h 35m total length

  • Types of data: categorical, numeric, etc5:22

    Understand what data are, how measurement devices produce data, and two types—numerical and categorical—through examples like temperature, height, education level, and movie genre, and why data type matters for analysis.

  • Populations, samples, case reports, and anecdotes5:27

    Explore population data, sample data, and case reports, distinguishing when data come from all members, a random subset, or a single person, and how trust and generalization vary.

  • Visualizing data5:18

    Explore core data visualization methods for categorical and numerical data, including bar plots, histograms, lines vs bars, and pie charts that sum to 100 percent.

  • Measures of central tendency (mean, median, mode)5:34

    Understand measures of central tendency—mean, median, and mode—and when each best reflects data clustering. Use mean for typical data, median for skewed distributions, and mode for categorical data.

  • Measures of dispersion (variance, standard deviation)3:12

    Learn how dispersion measures quantify variability around the mean by comparing two distributions with the same center but different widths, using variance and its square root, the standard deviation.

  • Data normalizations4:04

    Explore data normalization by transforming disparate measurements into comparable scales, from linear shifts that preserve relationships to nonlinear transformations that reshape distributions, scaling data to zero and one.

Requirements

  • None!

Description

Welcome to this short course on statistics literacy! The purpose of this course is to teach you about basic statistics terminology and foundational concepts. You will learn the meaning of key terms in statistics, such as p-value, ANOVA, variance, t-test, etc.

If you are brand-new to statistics, then this is the right course for you. It’s a beginner-level course, so if you’ve already taken a statistics course, or read a statistics book, then you might find all of the material covered in this course to be familiar. In that case, you can browse through the course and see if there are some concepts you need to brush up on. You don’t necessarily need to go through the entire course in order — you can skip around to the videos you are most interested in learning from.

By the end of this course, you will feel more comfortable talking about and reading about commonly used statistical analysis methods. You’ll also be able to engage in conversations with people who focus on technical or statistical issues, business analytics, and so on.

Please note that this course does not cover the math of the analyses, nor software to perform statistical analyses. I show a few basic formulas, but the focus is much more on conceptual understanding than mathematical detail.

Who this course is for:

  • People unfamiliar with statistics but who want to learn the basics!

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