Exploratory Data Analysis in Python
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.
Codecademy courses have been taken by employees at
- 1Learn about exploratory data analysis and what it is used for.
- 2Learn how to use exploratory data analysis (EDA) to inform data inspection, cleaning, and validation.
- 3Learn how to explore a single feature in a dataset using summary statistics and simple data visualizations.
- 4Learn how to use aggregate functions in pandas to calculate tables of summary statistics.
- 5Learn how to investigate whether there is an association between two variables.
What you'll create
Portfolio projects that showcase your new skills
How you'll master it
Stress-test your knowledge with quizzes that help commit syntax to memory
— Madelyn, Pinterest
I know from first-hand experience that you can go in knowing zero, nothing, and just get a grasp on everything as you go and start building right away.
Course Description
Learn about exploratory data analysis (EDA) techniques.
Details
Earn a certificate of completion
10 hours to complete in total
Beginner
1 article
1 article, 1 project
Learn how to explore a single feature in a dataset using summary statistics and simple data visualizations.
1 lesson, 1 quiz, 1 project
3 lessons, 1 quiz, 1 project
4 articles, 1 project
3 articles