____________________________________________ 🔔 *Don’t forget to LIKE & SUBSCRIBE* for more Python & Data Analysis tutorials! 💬 *Have a question* ? Drop it in the comments! 💎 *Want to Buy Me A Coffee* : *What you’ll learn* Unlock the full potential of your data with this comprehensive tutorial on Exploratory Data Analysis (EDA) using Python in Jupyter Notebook. *This step-by-step guide covers* • Loading and Inspecting Data: Learn how to import datasets and understand their structure. • Handling Missing Values: Discover techniques to identify and manage incomplete data. • Univariate Analysis: Explore individual variables to uncover underlying patterns. • Bivariate Analysis: Examine relationships between two variables for deeper insights. • Data Visualization: Utilize libraries like Matplotlib and Seaborn to create insightful plots. • Correlation Analysis: Understand how variables interact with each other. By the end of this tutorial, you'll be equipped with the skills to perform EDA confidently, setting a strong foundation for any data science project. *Resources* • Dataset Used: " • Anaconda Installation Video to use Jupyter Notebook: *Continue your learning with Python* *Get free resources to continue learning: * ====== *Great Books For Mastering Data Science and Data Cleaning* ====== Python For Data Analysis: Python Data Science Handbook: Hands-On Machine Learning with Scikit-Learn & TensorFlow: Python Machine Learning by Sebastian Raschka: Modern Python Cookbook: updated: ⏳ *Timestamps* ⏳ 00:00 Introduction 00:59 What we will cover in this video 01:27: What is Explanation of Master Exploratory Data Analysis (EDA) 02:18 Step 1: Import libraries & Set visual plot 05:10 Step 2: Load the dataset 06:00 Step 3: Basic Overview of the dataset 07:16 How to view the number of columns and rows using Python 07:28 How to view the datatypes for each variable in the dataset using Python code 07:53 How to view the summary statistics of a numerical variables in the dataset using Python. 09:03 Visualize the missing values 10:52 Step 5: Univariate Analysis 16:16 Create a Countplot for categorical column 19:00 Using a Groupby to compare rates in Python 20:32 Step 6: Bivariate Analysis (Comparing Two Variables with a barchart) 24:06 Bivariate Analysis (Comparing Two Variables with a scatterplot) 28:04 Step 7: Heat Map - Variable Correlation Matrix in Python 31:42 Closing and thank you for watching. #dataanlysis #pythonforbeginners #jupyternotebook #exploratorydataanalysis #eda #datascience #pythontutorial *Disclaimer*: This content is for educational purposes only. Affiliate links may be included, and I may earn a small commission at no extra cost to you. Thank you for supporting the channel!