Course
Exploratory Data Analysis in Python
IntermediateSkill Level
Updated 04/2026Start Course for Free
Included withPremium or Teams
PythonExploratory Data Analysis4 hr14 videos49 Exercises4,150 XP100K+Statement of Accomplishment
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Using data on unemployment figures and plane ticket prices, you’ll leverage Python to summarize and validate data, calculate, identify and replace missing values, and clean both numerical and categorical values. Throughout the course, you’ll create beautiful Seaborn visualizations to understand variables and their relationships.
Finally, the course will show how exploratory findings feed into data science workflows by creating new features, balancing categorical features, and generating hypotheses from findings.
By the end of this course, you’ll have the confidence to perform your own exploratory data analysis (EDA) in Python.You’ll be able to explain your findings visually to others and suggest the next steps for gathering insights from your data!The videos contain live transcripts you can reveal by clicking "Show transcript" at the bottom left of the videos. The course glossary can be found on the right in the resources section.To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.
Feels like what you want to learn?
Start Course for FreeWhat you'll learn
- Engineer and transform features from categorical and text data.
- Evaluate and manage outliers to maintain representative data distributions.
- Explore and validate datasets to assess structure and data quality.
- Extend EDA by generating features and evaluating representativeness.
- Identify, assess, and address missing or inconsistent data.
Prerequisites
Introduction to Statistics in PythonIntroduction to Data Visualization with Seaborn1
Getting to Know a Dataset
What's the best way to approach a new dataset? Learn to validate and summarize categorical and numerical data and create Seaborn visualizations to communicate your findings.
2
Data Cleaning and Imputation
Exploring and analyzing data often means dealing with missing values, incorrect data types, and outliers. In this chapter, you’ll learn techniques to handle these issues and streamline your EDA processes!
3
Relationships in Data
Variables in datasets don't exist in a vacuum; they have relationships with each other. In this chapter, you'll look at relationships across numerical, categorical, and even DateTime data, exploring the direction and strength of these relationships as well as ways to visualize them.
4
Turning Exploratory Analysis into Action
Exploratory data analysis is a crucial step in the data science workflow, but it isn't the end! Now it's time to learn techniques and considerations you can use to successfully move forward with your projects after you've finished exploring!
Exploratory Data Analysis in Python
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Enroll NowFAQs
What topics does this course cover?
This course will cover the process of exploring and analyzing data, from understanding what’s included in a dataset to incorporating exploration findings into a data science workflow. You’ll learn how to summarize and validate data, calculate missing values and clean both numerical and categorical values, and create effective visualizations to represent your data. Additionally, you’ll explore relationships across numerical, categorical, and DateTime data to gain useful insights.
Who will benefit from this course?
This course would be invaluable to anyone working in data science, machine learning, or business intelligence. People in roles such as data analyst, data scientist, information manager, or research engineer would benefit immensely from this course.
Will I receive a certificate at the end of the course?
Yes, you’ll receive a personalized certificate of completion from DataCamp upon completing the course.
How long will it take to finish this course?
On average, it takes students 4 hours to complete all the lectures and exercises in this course.
What will I learn at the end of the course?
By the end of this course, you’ll have the confidence to perform your own exploratory data analysis (EDA) in Python and explain your findings visually. You’ll also know how to best incorporate your findings into a data science workflow to gather more useful insights!
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