Exploratory Data Analysis (EDA) Crash Course in Python

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