Mastering Exploratory Data Analysis with Python Pandas: Step-by-Step Guide 📊

Link to Kaggle dataset: https://www.kaggle.com/datasets/maharshipandya/-spotify-tracks-dataset
Link to notebook: https://github.com/ds-with-uj/tutorials/blob/main/EDA.ipynb

In this comprehensive tutorial, dive into the world of exploratory data analysis (EDA) using Python Pandas and Seaborn. This video will equip you with the essential tools and techniques to uncover valuable insights from your datasets.

Throughout this tutorial, we'll cover:
00:00 Intro
00:13 Overview of EDA
2:06 Important Note
2:30 Overview of the dataset
4:26 Loading data and initial exploration with Pandas
8:41 Finding missing values
12:23 Finding duplicated data
16:50 Univariate analysis of qualitative variables
18:19 Univariate analysis of quantitative variables (start)
18:35 Statistic concept overview (central tendency, spread, skew, kurtosis)
22:03 Pandas describe function
23:01 Histogram and Box plots
26:11 Mulitvariate Analysis using correlations and heat maps (Seaborn)
30:20 Multivariate Analysis using pair plots (Seaborn)
34:44 Pairplots with 3 variables
39:32 Outro

This video covers the following:
- Introduction to Exploratory Data Analysis (EDA) and its significance in data science.
- Getting started with Pandas: Learn how to load, manipulate, and clean your datasets efficiently using Pandas DataFrames.
- Data Visualization with Seaborn: Explore the capabilities of Seaborn to create stunning visualizations that reveal patterns and relationships within your data.
- Basic statistical analysis: Use Pandas to compute descriptive statistics and gain a deeper understanding of your data's distribution.
- Handling missing data: Learn strategies to identify and deal with missing values effectively.
- Correlation analysis: Utilize Pandas and Seaborn to explore correlations between variables and uncover hidden relationships.
- Outlier detection: Learn how to identify and handle outliers that can skew your analysis results.