Statistical Techniques in Tableau
Take your reporting skills to the next level with Tableau’s built-in statistical functions.
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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
Univariate exploratory data analysis
FreeExploratory 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!
Welcome to the course!50 xpInterpreting histograms100 xpEDA in Tableau: tables and bar plots50 xpSuperstore data: table100 xpSuperstore data: bar plot100 xpEDA in Tableau: histograms50 xpSuperstore data: histogram promo100 xpSuperstore data: histogram bin size100 xpBox plots and distribution characteristics50 xpWhich visualization should you choose?100 xpEDA in Tableau: box plots50 xpSuperstore data: boxplot100 xpSuperstore data: compare box plots100 xp - 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.
Measures of spread50 xpStatistics: true of false100 xpTableau: summary cards and spread50 xpSuperstore data: summary card100 xpSuperstore data: standard deviation100 xpBonus question: sample vs population100 xpSuperstore data: variance100 xpStandard error and confidence intervals50 xpA bunch of statistics and estimates100 xpTableau: adding lines and distribution bands50 xpSuperstore data: high workload100 xpSuperstore data: confidence intervals100 xpSuperstore data: mean or median?100 xp - 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!
The relationship of two variables50 xpGuess the correlation100 xpTableau: trend lines50 xpDinosaurs: what to predict?100 xpDinosaurs: how to disaggregate?100 xpDinosaurs: adding a trend line100 xpAssessing a trend line50 xpModel predictions: rigorous or randomness?100 xpTableau: describing trend models50 xpDinosaurs: model summary100 xpDinosaurs: model by clade100 xpDinosaurs: confidence intervals100 xp - 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.
Forecasting50 xpForecasting: true or false?100 xpTableau: forecasting50 xpT-shirt sales: first forecast100 xpT-shirt sales: aggregating100 xpT-shirt sales: forecast adjustments100 xpClustering50 xpTo cluster or not to cluster?100 xpTableau: clustering50 xpWorld indicators: clustering countries100 xpWorld indicators: describing clusters100 xpWorld indicators: mapping the clusters100 xpWorld indicators: cluster results100 xpCongratulations!50 xp
In the following tracks
Data Analyst in TableauDatasets
Workbooks and DatasourcesCollaborators
Prerequisites
Introduction to Tableau
Maarten Van den Broeck
Senior Content Developer at DataCamp
Maarten is an aquatic ecologist and teacher by training and a data scientist by profession. He is also a certified Power BI and Tableau data analyst. After his career as a PhD 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.
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