Mastering Data Infrastructure with Python & SQL for Engineer

Build a Manufacturing Data Platform: Master Advanced SQL (Recursive/Window), Python (Pandas), and AWS RDS Deployment.

What you’ll learn
Design robust databases using normalization and composite primary keys to prevent data duplication and inconsistencies.
Master advanced SQL techniques like Recursive CTEs for BOM explosion and Window Functions for retrieving the latest unit prices.
Implement practical business logic with Python (Pandas), including data cleansing, MOQ rounding, and lead-time calculation.
Deploy a local database system to the cloud using Amazon RDS and understand the architecture of separating Writer and Reader instances.

Requirements
A Google account (to use Google Colab).
A computer with internet access (No local environment setup is required).
No prior experience with SQL or Python is necessary; we will start from the basics.
An AWS account is required only for the final section (optional, but recommended for the full experience).

Description
Turn raw manufacturing data into actionable insights with the power of Python and SQL.This is a practical, hands-on course designed to teach you how to build a data infrastructure from scratch, specifically tailored for Manufacturing and Supply Chain Management (SCM) scenarios.Most programming courses teach basic syntax using “clean data.” But in the real world, data is rarely clean. Engineers face distinct supplier data formats, inconsistent units (full-width/half-width characters), and complex hierarchical structures like Bills of Materials (BOM). This course focuses on tackling these “messy,” real-world data challenges.Using Google Colab (a browser-based development environment), you will learn not just how to write code, but why specific engineering designs are necessary. You will go from database design all the way to production deployment on the AWS Cloud.【What You Will Learn】Database Design Beyond Excel:Design schemas using “Composite Primary Keys” and “Normalization” to prevent data duplication and errors.Intermediate to Advanced SQL Techniques:Recursive Queries (Recursive CTE): Explode hierarchical BOM structures.Window Functions: Extract only the latest unit prices from historical data.Inventory Allocation: Calculate shortages by comparing required quantities against current stock.Implementing Business Logic with Python:Data Cleansing: Convert non-computable string data into numbers using Regular Expressions (Regex).Order Calculation: Automate rounding based on MOQ (Minimum Order Quantity) and calculate delivery dates considering lead times.Cloud Architecture (AWS):Migrate a local system to Amazon RDS.Understand the architecture strategy of separating “Writer” (Primary) and “Reader” (Replica) instances.【Curriculum Overview】Section 1: Introduction & Data Architecture OverviewSection 2: Environment Setup (Google Colab & SQLite)Section 3: Practical Database Design (DDL / Normalization)Section 4: Advanced SQL for Complex Extraction (Recursive Queries / Joins)Section 5: ETL & Business Logic with Python (Pandas / Numpy)Section 6:[Practical] Deployment to AWS Cloud & Architecture StrategyStop writing code that “just works.” Join this course to acquire practical, field-ready skills that you can apply immediately to your job.【About the Instructor】 After majoring in Information Engineering, I started my career at a Japanese automotive manufacturer. With 7.5 years of experience in SCM (Packaging, Procurement, Purchasing) and 6 years as an IT Consultant specializing in the manufacturing and logistics sectors, I have deep expertise in Inventory Management and ERP system development. Currently, I run an independent EdTech business and create educational content on Cloud Computing and Programming as a Udemy Instructor.Holder of all AWS Certifications.

Who this course is for
System Engineers and IT professionals working in the manufacturing, logistics, or supply chain (SCM) industries.
Data Analysts who want to move beyond Excel and automate complex data processing using SQL and Python.
Developers looking to learn practical backend logic for inventory management, BOM structures, and procurement.
Beginners who want to learn data engineering through a real-world, hands-on project rather than just syntax.

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