Skip to main content

Statistical Techniques in Tableau

Take your reporting skills to the next level with Tableau’s built-in statistical functions.

Start Course for Free
4 Hours18 Videos52 Exercises3,812 Learners4300 XP

Create Your Free Account



By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).

Loved by learners at thousands of companies

Course Description

Take your reporting skills to the next level with Tableau’s built-in statistical functions. Using drag and drop analytics, you'll learn how to perform univariate and bivariate exploratory data analysis and create regression models to spot hidden trends. Working with real-world datasets, you’ll also use machine learning techniques such as clustering and forecasting. It’s time to dig deeper into your data!

  1. 1

    Univariate exploratory data analysis


    Exploratory data analysis, or EDA, is a fundamental step when doing data research. Getting the first insights of your data is easy in Tableau: you’ll be creating and interpreting tables, bar plots, histograms, and box plots in no time!

    Play Chapter Now
    Welcome to the course!
    50 xp
    Interpreting histograms
    100 xp
    EDA in Tableau: tables and bar plots
    50 xp
    Superstore data: table
    100 xp
    Superstore data: bar plot
    100 xp
    EDA in Tableau: histograms
    50 xp
    Superstore data: histogram promo
    100 xp
    Superstore data: histogram bin size
    100 xp
    Box plots and distribution characteristics
    50 xp
    Which visualization should you choose?
    100 xp
    EDA in Tableau: box plots
    50 xp
    Superstore data: boxplot
    100 xp
    Superstore data: compare box plots
    100 xp
  2. 2

    Measures of spread and confidence intervals

    In this more conceptual chapter, you’ll dive deeper into the use of different measures of center and spread, and how they should be used in Tableau. You’ll learn about the use of the summary card, the difference between sample and population, and how variance, standard deviation, and confidence intervals can be calculated and visualized.

    Play Chapter Now
  3. 3

    Bivariate exploratory data analysis

    It's time to look at two variables at a time. Describing the relationship between two variables, or regression, is a great way to spot trends in your data. You'll learn how to find the best trend line, describe the trend model, and predict future observations, using dinosaur data!

    Play Chapter Now
  4. 4

    Forecasting and clustering

    In this last chapter, you’ll explore two more advanced statistical techniques: forecasting and clustering. Forecasting helps you detect recurring patterns in your time-series data, and can predict how these patterns will change in the future. With clustering, you’re able to detect patterns in unlabeled data, allowing you to slice and dice your dataset to reveal hidden insights.

    Play Chapter Now


Workbooks and datasources


hadrien-d4e73b49-bc29-46b7-a485-2f598f38e3b9Hadrien Lacroixsara-billenSara Billen
Maarten Van den Broeck Headshot

Maarten Van den Broeck

Content Developer at DataCamp

Maarten is an aquatic ecologist and teacher by training and a data scientist by profession. After his career as a Ph.D. researcher at KU Leuven, he wished that he had discovered DataCamp sooner. He loves to combine education and data science to develop DataCamp courses. In his spare time, he runs a symphonic orchestra.
See More

What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA