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Exploratory Data Analysis (EDA) in python - Part-1
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Video Description:
Exploratory Data Analysis (EDA) is a critical step in analyzing and making sense of datasets. In this video, we will learn the fundamentals of EDA using Python.
We will cover:
What is EDA, why it is important and the goals of EDA
Loading datasets in Python using Pandas
Summary statistics like mean, median, correlation, missing values
Data visualization and matplotlib basics - histograms, scatter plots, box plots
Using seaborn for statistical data visualization
Techniques like log transforms and outlier detection
Working with different types of data - numerical, categorical, time series
Hands-on example doing EDA on a sample dataset
EDA allows us to visualize datasets, uncover patterns, detect anomalies and gain useful insights. It enables better problem definition before applying machine learning models. This video will provide a solid understanding of exploratory data analysis in Python.
The key python libraries we will use include pandas, numpy, matplotlib, seaborn and jupyter notebooks. EDA is a key skill for data scientists, analysts and engineers. This tutorial will demonstrate practical techniques using real-world examples.
View part 2 to learn advanced EDA techniques like aggregation, pivoting, handling duplicate data and more.
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Explore other free courses here:
🔥 Streamlit course for dash boards and webapps for data science (playlist):
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🔥-Follow me on following platforms:
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#dataScience
---------------------------------------------------------------------------------------------------------------------------------------
for more details:
Join this channel to get access to perks:
#datascience
---------------------------------------------------------------------------------------------------------------------------------------
Video Description:
Exploratory Data Analysis (EDA) is a critical step in analyzing and making sense of datasets. In this video, we will learn the fundamentals of EDA using Python.
We will cover:
What is EDA, why it is important and the goals of EDA
Loading datasets in Python using Pandas
Summary statistics like mean, median, correlation, missing values
Data visualization and matplotlib basics - histograms, scatter plots, box plots
Using seaborn for statistical data visualization
Techniques like log transforms and outlier detection
Working with different types of data - numerical, categorical, time series
Hands-on example doing EDA on a sample dataset
EDA allows us to visualize datasets, uncover patterns, detect anomalies and gain useful insights. It enables better problem definition before applying machine learning models. This video will provide a solid understanding of exploratory data analysis in Python.
The key python libraries we will use include pandas, numpy, matplotlib, seaborn and jupyter notebooks. EDA is a key skill for data scientists, analysts and engineers. This tutorial will demonstrate practical techniques using real-world examples.
View part 2 to learn advanced EDA techniques like aggregation, pivoting, handling duplicate data and more.
---------------------------------------------------------------------------------------------------------------------------------------
---------------------------------------------------------------------------------------------------------------------------------------
---------------------------------------------------------------------------------------------------------------------------------------
Explore other free courses here:
🔥 Streamlit course for dash boards and webapps for data science (playlist):
---------------------------------------------------------------------------------------------------------------------------------------
🔥-Follow me on following platforms:
---------------------------------------------------------------------------------------------------------------------------------------
#dataScience
---------------------------------------------------------------------------------------------------------------------------------------
for more details:
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