Master EDA in Python: Quick & Easy Data Exploration Crash Course 📊

Exploratory data analysis (EDA) is exploring data and investigating its structure to discover patterns and spot anomalies in said patterns.

In this video:

00:00 - Introduction
00:44 - What is Exploratory Data Analysis (EDA)?
02:48 - The EDA Cheatsheet
07:23 - Before Doing the Graphical and Non-graphical EDA
09:20 - About the Heart Disease Dataset
10:26 - Preliminaries: Importing the Package, and Dowloading the Dataset Programmatically and Initial Exploration
13:32 - Descriptive Statistics- mean, median, mode, skew, kurtosis, Interquartile Range (IQR), (Non-graphical methods)
23:04 - Univariate Graphical Methods
23:28 - Histogram (Univariate Graphical Methods)
26:45 - Kernel Density Estimate Plot (kde) (Univariate Graphical Methods)
30:08 - Countplot (Univariate Graphical Methods)
30:51 - Boxplots (Univariate Graphical Methods)
34:07 - Barplots (One Categorical, One Quantitative, Multivariate)
37:16 - Side-by-side boxplots (One Categorical, One Quantitative, Multivariate)
39:25 - Categorical Plots (Catplots) - (Multiple Categorical, One Quantitative, Multivariate)
42:50 - Scatterplot - (Two Quantitative Variables, Multivariate)
44:57 - Pairplot of Seaborn (All Quantitative Variables, Multivariate)
46:57 - Correlation Heatmap (All Quantitative Variables, Multivariate)
49:02 - Multivariate, Non-graphical methods
51:05 - Key Takeaways


Links in the video:

The Heart Disease Dataset (UCI Machine Learning Repository) - https://archive.ics.uci.edu/dataset/45/heart+disease

URL to download the UCI Dataset through Python:
https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data

Read my article:
https://medium.com/towards-data-science/dramatically-improve-your-exploratory-data-analysis-eda-a2fc8c851124