Exploratory Data Analysis with Python (EDA) | Descriptive Statistics | Univariate Analysis | Outlie

Exploratory data analysis (EDA) is used by data Analyst and/or scientists to analyze and investigate data sets and summarize their main characteristics, often employing data visualization methods.
It helps determine how best to manipulate data sources to get the answers you need, making it easier for data scientists to discover patterns, spot anomalies, test a hypothesis, or check assumptions.
In this video, we will use Python to carry out the EDA on AdventureWorks dataset. We will cover and see practical examples of; Descriptive statistics, Univariate, Bivariate Analysis, detect and remove Outliers and spot Anomalies.

Set up Python and Jupyter environment: https://www.youtube.com/watch?v=B0G-44dqHRM

Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
. maximize insight into a data set;
. uncover underlying structure;
. extract important variables;
. detect outliers and anomalies;
. test underlying assumptions;


GitHub link to Notebook and dataset:
https://github.com/hnawaz007/pythondataanalysis/tree/main/EDA

John Tukey's Exploratory Data Analysis:
http://www.ru.ac.bd/wp-content/uploads/sites/25/2019/03/102_05_01_Tukey-Exploratory-Data-Analysis-1977.pdf

Packit: Hands-On Exploratory Data Analysis with Python:
https://www.packtpub.com/product/hands-on-exploratory-data-analysis-with-python/9781789537253

Topics in this video (click to jump around):
==================================
0:00 Introduction of Exploratory Data Analysis
4:29 Import libraries
5:32 Check Data Types
5:48 Select columns from dataframe
6:13 Rename dataframe columns
6:32 What are variables and observations?
8:59 Basic data statistics
10:45 Data distribution/ Skewness
13:25 Detect and remove outliers
18:36 Drop duplicates and remove nulls
19:17 Univariate Analysis and Frequency Table
21:11 Bivariate/Multivariate Analysis
26:14 Correlation of two numeric variables
30:48 Spot Anomalies

#Python #ExploratoryDataAnalysis #DataAnalysis
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