Exploratory Data Analysis in Power BI

Overview

Learn how to build impactful reports with Power BI’s Exploratory Data Analysis (EDA) that uncover insights faster and drive business value.

Enhance your reports with Power BI's Exploratory Data Analysis (EDA). You'll start by using descriptive statistics to spot outliers, identify missing data, and apply imputation techniques to fill gaps in your dataset. You’ll then learn how EDA in Power BI can help you discover the relationships between variables—both categorical and continuous— by using basic statistical measures and box and scatter plots.

Syllabus

Initial Exploratory Data Analysis in Power BI

You’ll begin this Exploratory Data Analysis (EDA) course by learning how to use descriptive statistics and identify missing data, and apply imputation techniques to fill the gaps in your data.

Distributions and Outliers

In the second chapter of this course you'll learn how to identify and address outliers within the dataset. You will build histograms to analyze distributions and use winsorizing to remove outliers.

EDA with Categorical Variables

Now it’s time to explore the relationships between categorical variables using proportions. You’ll then use box plots and descriptive statistics to determine how a continuous variable is influenced by a categorical one.

Relationships between Continuous Variables

In the final chapter, you’ll dive into scatter plots to analyze the relationship between two continuous variables and calculate the correlation coefficient.

Taught by

Maarten Van den Broeck and Jacob Marquez

Reviews

4.6 rating at DataCamp based on 29 ratings

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