What is exploratory data analysis?

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.

What can you do with exploratory data analysis?

Exploratory data analysis gives you a better understanding of the data you’re working with. Not only will you learn about where it was sourced and the different variables it contains, but you can also get an overview of potential missing data, patterns, and trends.

What is an example of exploratory data analysis?

After receiving a new dataset, exploratory analysis might involve going through your data, noting any observations (i.e., obvious patterns or trends) or anomalies, and potentially visualizing your findings to share with others.

What kind of jobs use exploratory data analysis?

Everyone who works with data does some form of exploratory data analysis, but it’s generally a task for Data Scientists and Analysts.

What else should I study if I am learning about exploratory data analysis?

After learning how to explore your data for general observations and anomalies, the next step is learning how to clean data. You’ll come across a lot of missing or erroneous data in your work, and you’ll need to tidy it up before you can put it to good use.

Course

Exploratory Data Analysis in Python

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

This course includes

7,088 learners enrolled

This course includes

Skill level
Beginner
Time to complete
Average based on combined completion rates — individual pacing in lessons, projects, and quizzes may vary

6 hours
Projects
5
Prerequisites
None

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.

Syllabus

5 lessons • 5 projects • 3 quizzes

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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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