Python and SQL for Manufacturing Training

Comprehensive 4-day, hands-on training program focused on Python + SQL workflows for manufacturing quality analytics and SPC visualization.

Author

Prakash Ukhalkar
Assistant Professor (MCA)
Researcher in Data Science and Machine Learning

Training Overview

• Duration: 4 days
• Session length: 2 hours/day
• Total training time: 8 hours
• Audience: Manufacturing professionals with basic Python knowledge
• Environment: Python 3.10+, SQLite, VS Code, Jupyter notebooks
• Capstone: Excel-to-SPC Manufacturing Quality Dashboard

Learning Outcomes

By the end of this training, participants will be able to:

• Connect Python to SQLite and work with manufacturing datasets
• Create and manage relational tables for production and quality data
• Write SQL queries for filtering, aggregation, joins, and subqueries
• Build manufacturing charts and SPC visuals using Python libraries
• Detect out-of-control behavior and summarize process capability
• Deliver an end-to-end data pipeline from Excel to dashboard outputs

Quick Start (Under 5 Minutes)

1. Create and activate a virtual environment.

Windows PowerShell:

py -3.10 -m venv mfg-sql-py-venv
.\mfg-sql-py-venv\Scripts\Activate.ps1
python -m pip install --upgrade pip
pip install -r requirements.txt

macOS / Linux / WSL:

python3.10 -m venv mfg-sql-py-venv
source mfg-sql-py-venv/bin/activate
python -m pip install --upgrade pip
pip install -r requirements.txt

2. Launch Jupyter and run setup_check.ipynb first to verify environment, kernel, and core package readiness.
3. Open day1_sql_foundations/1.1_sqlite_connection.ipynb and continue the notebook sequence.

Repository Structure

mfg-python-sql-training/
|-- README.md
|-- LICENSE
|-- requirements.txt
|-- setup_check.ipynb
|-- day1_sql_foundations/
|   |-- 1.1_sqlite_connection.ipynb
|   |-- 1.2_database_creation_insertion.ipynb
|   |-- data/
|   `-- solutions/
|-- day2_querying_filtering/
|   |-- 2.1_select_filter_sort.ipynb
|   |-- 2.2_joins_subqueries.ipynb
|   |-- data/
|   `-- solutions/
|-- day3_visualization/
|   |-- 3.1_visualization_principles.ipynb
|   |-- 3.2_spc_charts.ipynb
|   |-- data/
|   `-- solutions/
|-- day4_capstone/
|   |-- 4.1_capstone_guided.ipynb
|   |-- 4.2_capstone_challenge.ipynb
|   |-- data/
|   `-- solutions/
|       `-- complete_capstone_solution.ipynb
`-- docs/
    |-- trainer_delivery_checklist.md
    `-- manufacturing_python_sql_training_plan.docx

Notebook Sequence

1. setup_check.ipynb (run first: verifies Python environment and notebook dependencies)
2. day1_sql_foundations/1.1_sqlite_connection.ipynb
3. day1_sql_foundations/1.2_database_creation_insertion.ipynb
4. day2_querying_filtering/2.1_select_filter_sort.ipynb
5. day2_querying_filtering/2.2_joins_subqueries.ipynb
6. day3_visualization/3.1_visualization_principles.ipynb
7. day3_visualization/3.2_spc_charts.ipynb
8. day4_capstone/4.1_capstone_guided.ipynb
9. day4_capstone/4.2_capstone_challenge.ipynb
10. day4_capstone/solutions/complete_capstone_solution.ipynb

Environment Setup (Detailed)

Use the Quick Start section above for the fastest path. This section is a reference for trainers and participants who need a setup recap.

Windows PowerShell

py -3.10 -m venv mfg-sql-py-venv
.\mfg-sql-py-venv\Scripts\Activate.ps1
python -m pip install --upgrade pip
pip install -r requirements.txt

macOS / Linux / WSL

python3.10 -m venv mfg-sql-py-venv
source mfg-sql-py-venv/bin/activate
python -m pip install --upgrade pip
pip install -r requirements.txt

Required Packages

Dependencies are defined in requirements.txt using minimum compatible versions.

Current requirements:

• pandas>=2.1.0
• openpyxl>=3.1.0
• matplotlib>=3.8.0
• seaborn>=0.13.0
• plotly>=5.18.0
• scipy>=1.11.0
• numpy>=1.26.0
• jupyterlab>=4.0.0

Daily Training Plan

Day 1: SQL Foundations

• SQLite connection and execution flow
• Table design for production and quality records
• Data insertion and validation

Day 2: Querying and Data Retrieval

• SELECT, WHERE, ORDER BY, LIMIT
• GROUP BY and HAVING
• JOINs and subqueries for multi-table analytics

Day 3: Visualization and SPC

• Chart selection for manufacturing questions
• Matplotlib and Seaborn implementation patterns
• X-bar and R-chart fundamentals and chart construction

Day 4: Capstone Project

• Excel ingestion and data cleaning
• Excel ingestion uses openpyxl for .xlsx workflow support
• SQL analysis pipeline
• SPC dashboard generation and challenge extension

Capstone Deliverables

Expected outputs from the Day 4 capstone:

• mfg_quality.db
• quality_dashboard.png
• interactive_dashboard.html
• Executed capstone notebook with results

Trainer Assets

• Delivery checklist: docs/trainer_delivery_checklist.md
• Training plan source: docs/manufacturing_python_sql_training_plan.docx

Pre-Generated Data Files

This repository already includes starter datasets in each day folder so notebooks can run immediately without additional data preparation.

Recommended Workflow

• Start with setup_check.ipynb
• Complete notebooks in sequence
• Attempt exercises before checking solution guidance
• Use Day 4 challenge notebook for independent validation

License

This project is licensed under the MIT License. See the LICENSE file for details.

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About the Author

Author	Prakash Ukhalkar
Role	Assistant Professor (MCA) · Researcher in Data Science and Machine Learning
GitHub	@prakash-ukhalkar
Repository	mfg-python-sql-training
License	MIT

Provided for educational use in manufacturing analytics training programmes.
For questions, corrections, or contributions, open an issue or pull request on GitHub.

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_{© 2026 Prakash Ukhalkar · Python and SQL for Manufacturing · MIT License}

Acknowledgments

This repository is designed for practical, manufacturing-first learning with a balance of SQL rigor, Python implementation, and decision-oriented visualization.