Exploratory Data Analysis in Python

Learn how to use exploratory data analysis (EDA) techniques in Python to evaluate, summarize, and visualize your data.

4,775 learners enrolled
  • Skill level

  • Time to complete

    Approx. 10 hours
  • Certificate of completion

    Included with paid plans
  • Prerequisites


About this course

In this course, you will learn about exploratory data analysis techniques in Python, including:

  • EDA for data preparation
  • Summary statistics
  • Data visualization techniques
  • EDA prior to building a machine learning model

Prior to taking this course, you should have some knowledge of base Python and experience with pandas DataFrames.

Exploratory data analysis is an important part of any Data Scientist or Analyst’s workflow, so we highly recommend this course for anyone who is interested in working with data.


5 lessons • 5 projects • 3 quizzes
The platform

Hands-on learning

Don’t just watch or read about someone else coding — write your own code live in our online, interactive platform. You’ll even get AI-driven recommendations on what you need to review to help keep you on track.
Animated demonstration of running code in Codecademy's learning environment
Meet the creator of the course
Nitya Mandyam
Senior Curriculum Developer at Codecademy
Nitya is a Senior Curriculum Developer at Codecademy. Nitya has created courses and skill paths relating to machine learning/AI across the Data Science catalog such as Feature Engineering, ML Fundamentals, Intermediate ML, Recommender Systems, Building a ML Pipeline.

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Unlock additional features with a paid plan

  • Practice Projects

    Guided projects that help you solidify the skills and concepts you're learning.
  • Assessments

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  • Certificate of Completion

    Earn a document to prove you've completed a course or path that you can share with your network.

Join over 50 million learners and start Exploratory Data Analysis in Python today!


Frequently asked questions about Exploratory Data Analysis in Python

  • You need to understand your data before you can put it to good use. Exploratory data analysis (or EDA) involves familiarizing yourself with a new dataset. Generally you’re just looking for a broad understanding and insights into possible patterns and relationships within the data.