End-to-End Data Analyst Project for Beginners: Walmart Sales Data Analysis with SQL & Python #09/10

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Welcome to our beginner-friendly, end-to-end data analyst project! In this video, we’ll dive into the Walmart Sales Data Analysis project, guiding you through each step with SQL and Python. This is part #09 in a 10-part series, designed to help you build a solid foundation in data analysis from start to finish.
We’ll work with real sales data from Walmart, covering essential skills like data extraction, date formatting, querying, data cleaning, and analysis. Using MySQL, PostgreSQL, Python, and additional data from Kaggle and APIs, this tutorial gives you hands-on experience with the tools, techniques, and workflows needed for data analyst roles.
🚀 What You’ll Learn:
Data Extraction: Importing Walmart sales data and additional sources from APIs and Kaggle.
SQL Date Handling: Transform date formats, extract day names, and manipulate dates using MySQL and PostgreSQL.
Data Cleaning with Python: Clean and preprocess data for analysis using Python libraries like Pandas.
Advanced SQL Queries: Use SQL to uncover trends, sales patterns, and customer behaviors.
Visualizing Data: Use Python to create meaningful charts and visualizations for a comprehensive view of sales insights.
🛠️ Tools & Resources Used:
Languages: SQL, Python
Databases: MySQL, PostgreSQL
Libraries: Pandas, Requests, Matplotlib
Data Sources: Walmart data from Kaggle, API
Skills: Data cleaning, date manipulation, API integration, SQL querying, data visualization

📝 Chapters:
0:00 - 4:00 - Introduction to the Project and Tools
4:35 - 15:00 - Environment Setup: Loading and Integrating Kaggle and API Data
15:45 - 20:00 - Data Exploration with Pandas
21:00 - 31:00 - Data Cleaning Techniques using Pandas
32:00 - 52:00 - Exporting Data to MySQL and PostgreSQL
53:00 - 55:45 - Exploratory Data Analysis (EDA) with SQL
57:00 - 58:00 - Question 1
58:30 - 1:02:45 - Question 2
1:03:00 - 1:07:00 - Question 3
1:07:16 - 1:09:00 - Question 4
1:09:16 - 1:11:00 - Question 5
1:11:16 - 1:13:15 - Question 6
1:13:31 - 1:16:00 - Question 7
1:17:00 - 1:24:00 - Question 8
1:25:00 - 1:36:00 - Question 9
1:36:30 - 1:39:30 - Project Publishing

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