What Is Exploratory Data Analysis?
Exploratory data analysis is a key step in the data analysis process. Explore how you can use this method, variations suited for different analyses, and which careers utilize this technique.
October 9, 2024
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Recommended experience
Intermediate level
Basic understanding of Pandas and Python and familiarity with Google Colab or Jupyter.
Recommended experience
Intermediate level
Basic understanding of Pandas and Python and familiarity with Google Colab or Jupyter.
Conduct exploratory data analysis.
Plot and analyze distributions, comparisons, composition, and relationships.
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Only available on desktop
Conduct exploratory data analysis with a systematic approach to investigate different aspects of your data: comparisons, relationships, compositions, and distributions. This guided project gives you a framework so you can conduct your own exploratory data analysis and make your work more professional and organized. The language is Python and the libraries used are seaborn, pandas, and matplotlib.
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Build a solid foundation with Matplotlib and Seaborn
Plot and analyze distributions within the data
Plot and analyze comparisons within the data
Practice Task - Self Assessment
Plot and analyze the composition of data
Plot and analyze the relationships in our data
Capstone - Exploratory Data Analysis (EDA)
Basic understanding of Pandas and Python and familiarity with Google Colab or Jupyter.
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Available only on desktop
This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.
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