The Google Guide to Exploratory Data Analysis - With Code

Overview

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Explore Google's guide to Exploratory Data Analysis through a practical demonstration using personal productivity data. Learn essential techniques for data cleaning, importing, and analyzing various data types, including handling missing values and categorical variables. Discover how to create insightful visualizations such as violin plots, histograms, and heatmaps. Dive into textual and date analysis, apply the Pareto Principle, and understand correlation matrices. Gain valuable skills in Python-based data analysis to enhance your understanding of working habits and improve productivity.

Syllabus

Intro
The Data
Getting Started
Data Import
Data Types
Missing Values
Total seconds
Violin Plot
Histogram
PdCut
Categorical attribute types
Categorical variables
Cardinality and Unique Counts
The Pareto Principle
Creating a Function
Percent of Total
Running Percent of Total
Category
Bundle
BaseUrl
Textual Analysis
Date Analysis
Histograms
Bar Chart
Dinner
Dates
Qualitative
DataFrame
Heatmap
Correlation Matrix
Data Import Table
Correlation Analysis