Advanced SQL with Python in Jupyter Notebook: Analyze Real-World E-commerce Data
Ready to master real-world SQL queries using Python in Jupyter Notebook? In this tutorial, you’ll use Python and SQLite to explore advanced SQL techniques on a real-world e-commerce dataset. Learn how to run queries, filter data, join tables, calculate revenue, and visualize results—all inside Jupyter Notebook!
What You’ll Learn:
• SQL Aggregations (SUM, AVG, GROUP BY, HAVING)
• Multi-table JOINs (INNER, LEFT)
• Subqueries
• Python with SQLite & pandas
• Real-world data analysis and visualization
Perfect for aspiring Data Analysts, Python Developers, and SQL learners!
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How to download Anaconda to use Jupyter Notebook for Python coding:
• How to Download Anaconda for Jupyter Noteb...
📦 Download the SQL Database used in this project:
👉 http://bit.ly/3YvcdAM
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Thank you for watching.
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⏳ Timestamps ⏳
00:00 Introduction
0:36 Opened Jupyter Notebook
00:58 Download the database file into Jupyter Notebook
01:37 Title the New Workbook In Jupyter Notebook
01:54 Step 1: Connect to a SQLite Database & import libraries
04:40 An explanation of the libraries used
05:24 Step 2: Look and review the Database Tables in Jupyter Notebook
07:06 Look at the Data structure and what kind of data it stores.
10:44 Step 3: Create a SQL query to find the top paying customers who spent the most.
14:18 Step 4: Visualize top paying customers (Bar Chart).
17:55 Step 5: Find and review the Products Not Ordered in the last 30 Days
22:28 Step 6: Create a SQL query to show the Monthly revenue by Country
26:16 Step 7: Visualize the monthly revenue by Month and country (line Plot).
29:22 Step 8: Create a Subquery to find the highest order in the store.
32:05 Step 9 Close the Database Connection
32:30 Ending statement. Thank you for watching!
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