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Exploratory Data Analysis in Power BI

Enhance your reports with Power BI's Exploratory Data Analysis (EDA). Learn what EDA is for Power BI and how it can help you extract insights from your data.

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3 Hours9 Videos26 Exercises2050 XPData Analyst Track

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Course Description

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.

  1. 1

    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.

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    What is exploratory data analysis?
    50 xp
    Steps to EDA
    100 xp
    Initial EDA of AirBnB listings
    50 xp
    Identify missing data
    100 xp
    Descriptive statistics for a variable
    100 xp
    Imputation for missing data
    100 xp
  2. 2

    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.

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  3. 3

    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.

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  4. 4

    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.

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In the following tracks

Data Analyst


carlrosseel-73112126-9ed2-4866-942a-96fbf265b088Carl Rosseel
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.
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Jacob Marquez Headshot

Jacob Marquez

Data Scientist at Microsoft

Jacob H. Marquez is an insatiable learner and lifelong builder. He is a data scientist by day, answering audacious questions to support customer experience and company goals. He is a serial hobbyist by day and night: being an educator, building a coffee recommendation app, drinking coffee, writing on Medium, and amateur cycling and muay thai. He has a bachelor's in psychology and a master's in computational analytics (2024).
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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