Time to completeApprox. 10 hours
Certificate of completionIncluded with paid plans
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.
Syllabus5 lessons • 5 projects • 3 quizzes
Hands-on learningDon’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.
Projects in this course
EDA: Diagnosing DiabetesInspect, clean, and validate a dataset on diabetes prevalence among females of Pima Indian heritage.
Exploring Student DataSummarize the features of a dataset about student performance at two different schools.
A/B Testing for ShoeFly.comAnalyst the A/B test data from ShoeFly.com using aggregate measures.
Reviews from learners
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- Brilliant learning experience. Very interactive. Literally a game changer if you're learning on your own.John-AndrewCodecademy Learner @ USA
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Practice ProjectsGuided projects that help you solidify the skills and concepts you're learning.
AssessmentsAuto-graded quizzes and immediate feedback help you reinforce your skills as you learn.
Certificate of CompletionEarn a document to prove you've completed a course or path that you can share with your network.
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.