What is Exploratory Data Analysis? Steps & Examples
Nicolas Azevedo
Data Scientist and Machine Learning Engineer
Originally published on Jun 10, 2021Last updated on Jul 21, 2023
Key Takeaways
What is exploratory data analysis explain with an example?
Exploratory Data Analysis (EDA) in Data Science is a step in the analysis process that uses several techniques to visualize, analyze, and find patterns in the data. John Turkey, who developed the EDA method, likened it to detective work because you have to dig for clues and evidence before making any assumptions about the outcome.
How do I start analyzing data?
Univariate Analysis is the simplest form of data analysis. 'Uni' refers to analyzing one individual attribute to understand the position of the data in the dataset by the central tendency measures and the sparseness of that data by the dispersion measures. This includes analyzing, mode, median, mean, and quantile and dispersion measures like variance, standard deviation, amplitude, Interquartile range, and coefficient of variation.
Is exploratory data analysis Qualitative or quantitative?
It can be both depending upon your dataset or the research you're using.
Looking to hire?
Join our newsletter
Join thousands of subscribers already getting our original articles about software design and development. You will not receive any spam, just great content once a month.
Read Next
Machine Learning
What is Bias in Machine Learning? Real-World Examples That Show the Impact of AI Bias
As artificial intelligence, or AI, increasingly becomes a part of our everyday lives, the need for understanding the systems behind this technology as well as their failings, becomes equally important. It’s simply not acceptable to write AI off as a foolproof black box that outputs sage advice. In reality, AI can be as flawed as its creators, leading to negative outcomes in the real world
Omar Trejo
Senior Data Scientist
Data Science
What is Exploratory Data Analysis? Steps & Examples
One of the most important things you can do when approaching a data science project is really understand the dataset you’re working with as a first step. Without a proper data exploration process in place, it becomes much more challenging to identify critical issues or successfully carry out a deeper analysis of the dataset. Exploratory Data Analysis (EDA) in Data Science is a step in
Nicolas Azevedo
Data Scientist and Machine Learning Engineer