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.

Introduction to Exploratory Data Analysis in Power BI

Enhance your reports with Power BI's Exploratory Data Analysis (EDA)!

This beginner course for data analysts covers foundational aspects of EDA, including descriptive statistics, handling missing data, analyzing distributions, and identifying outliers. You'll learn to use histograms, box plots, and scatter plots to visualize data, interpret distributions, and understand relationships between variables.

Apply Statistical Techniques

Explore how to manage categorical and continuous variables, use advanced visualization techniques, and calculate correlation coefficients. Interactive exercises and real-world examples ensure you can apply these skills to analyze and interpret your data effectively.

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.