Coding-For-MBA

Coding for MBA

A 67-day applied Python, analytics, and machine learning curriculum designed for business professionals.

Transform your business acumen into technical capability with this comprehensive, hands-on curriculum. Each lesson is self-contained and builds toward end-to-end data fluency—from programming fundamentals to modern ML operations.

🚀 Quick Start

# Clone and setup
git clone https://github.com/saint2706/Coding-For-MBA.git
cd Coding-For-MBA
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install -r requirements.txt

Optional database dependencies:

pip install mysql-connector-python psycopg2-binary pymongo

📖 Documentation

View the full documentation site →

The documentation includes interactive examples, detailed explanations, and downloadable materials for each lesson.

🗺️ Curriculum Overview

The curriculum is organized into four progressive phases over 67 days:

Phase	Days	Focus
Phase 1	01-20	Python foundations, data structures, file handling
Phase 2	21-39	Data workflows, databases, APIs, statistics, visualization
Phase 3	40-54	ML fundamentals, neural networks, NLP
Phase 4	55-67	Advanced ML, MLOps, transformers, deployment

📘 See full curriculum roadmap →

💻 Working with Lessons

Each Day_XX_* folder contains:

• README.md - Lesson content and explanations
• Python scripts (.py) - Executable code examples
• Jupyter notebooks (.ipynb) - Interactive versions
• Solutions (where applicable) - Reference implementations

Launch Jupyter for interactive learning:

jupyter notebook
# Navigate to any Day_XX folder and open the .ipynb file

📚 All Lessons

Quick access to all 67 lessons:

Day	Lesson
Day 01	📘 Day 1: Python for Business Analytics - First Steps
Day 02	📘 Day 2: Storing and Analyzing Business Data
Day 03	📘 Day 3: Operators - The Tools for Business Calculation and Logic
Day 04	📘 Day 4: Working with Text Data - Strings
Day 05	📘 Day 5: Managing Collections of Business Data with Lists
Day 06	📘 Day 6: Tuples - Storing Immutable Business Data
Day 07	📘 Day 7: Sets - Managing Unique Business Data
Day 08	📘 Day 8: Dictionaries - Structuring Complex Business Data
Day 09	📘 Day 9: Conditionals - Implementing Business Logic
Day 10	📘 Day 10: Loops - Automating Repetitive Business Tasks
Day 11	📘 Day 11: Functions - Creating Reusable Business Tools
Day 12	📘 Day 12: List Comprehension - Elegant Data Manipulation
Day 13	📘 Day 13: Higher-Order Functions & Lambda
Day 14	📘 Day 14: Modules - Organizing Your Business Logic
Day 15	📘 Day 15: Exception Handling - Building Robust Business Logic
Day 16	📘 Day 16: File Handling for Business Analytics
Day 17	📘 Day 17: Regular Expressions for Text Pattern Matching
Day 18	📘 Day 18: Classes and Objects - Modeling Business Concepts
Day 19	📘 Day 19: Working with Dates and Times
Day 20	📘 Day 20: Python Package Manager (pip) & Third-Party Libraries
Day 21	📘 Day 21: Virtual Environments - Professional Project Management
Day 22	📘 Day 22: NumPy - The Foundation of Numerical Computing
Day 23	📘 Day 23: Pandas - Your Data Analysis Superpower
Day 24	📘 Day 24: Advanced Pandas - Working with Real Data
Day 25	📘 Day 25: Data Cleaning - The Most Important Skill in Analytics
Day 26	📘 Day 26: Practical Statistics for Business Analysis
Day 27	📘 Day 27: Data Visualization - Communicating Insights
Day 28	📘 Day 28: Advanced Visualization & Customization
Day 29	📘 Day 29: Interactive Visualization with Plotly
Day 30	📘 Day 30: Web Scraping - Extracting Data from the Web
Day 31	📘 Day 31: Working with Databases in Python
Day 32	📘 Day 32: Connecting to Other Databases (MySQL & MongoDB)
Day 33	📘 Day 33: Accessing Web APIs with requests
Day 34	📘 Day 34: Building a Simple API with Flask
Day 35	🌐 Day 35: Flask Web Framework
Day 36	📊 Day 36 – Capstone Case Study
Day 37	🎉 Day 37: Conclusion & Your Journey Forward 🎉
Day 38	Day 38: Math Foundations - Linear Algebra
Day 39	Day 39: Math Foundations - Calculus
Day 40	Day 40: Introduction to Machine Learning & Core Concepts
Day 41	Day 41 · Supervised Learning – Regression
Day 42	Day 42 · Supervised Learning – Classification (Part 1)
Day 43	Day 43 · Supervised Learning – Classification (Part 2)
Day 44	Day 44: Unsupervised Learning
Day 45	Day 45: Feature Engineering & Model Evaluation
Day 46	Day 46: Introduction to Neural Networks & Frameworks
Day 47	Day 47: Convolutional Neural Networks (CNNs) for Computer Vision
Day 48	Day 48: Recurrent Neural Networks (RNNs) for Sequence Data
Day 49	Day 49: Natural Language Processing (NLP)
Day 50	Day 50: MLOps - Model Deployment
Day 51	Day 51 – Regularised Models
Day 52	Day 52 – Ensemble Methods
Day 53	Day 53 – Model Tuning and Feature Selection
Day 54	Day 54 – Probabilistic Modeling
Day 55	Day 55 – Advanced Unsupervised Learning
Day 56	Day 56 – Time Series and Forecasting
Day 57	Day 57 – Recommender Systems
Day 58	Day 58 – Transformers and Attention
Day 59	Day 59 – Generative Models
Day 60	Day 60 – Graph and Geometric Learning
Day 61	Day 61 – Reinforcement and Offline Learning
Day 62	Day 62 – Model Interpretability and Fairness
Day 63	Day 63 – Causal Inference and Uplift Modeling
Day 64	Day 64 – Modern NLP Pipelines
Day 65	Day 65 – MLOps Pipelines and CI/CD Automation
Day 66	Day 66 – Model Deployment and Serving Patterns
Day 67	Day 67 – Model Monitoring and Reliability Engineering

⭐ Featured Lessons

Explore some of the standout lessons that demonstrate production-ready patterns:

• Day 50 – MLOps - Model training, saving, and deployment patterns
• Day 58 – Transformers - Attention mechanisms and Hugging Face integration
• Day 60 – Graph Learning - GraphSAGE and attention message passing
• Day 36 – Case Study - End-to-end analytics workflow

📘 View all featured lessons →

🧪 Testing & Development

Running Tests

pip install -r requirements-dev.txt
pytest

Tests cover 233+ scenarios across all lesson phases with 40%+ coverage requirements.

Code Formatting

make format  # Auto-format Python, notebooks, and Markdown
make lint    # Check formatting without changes

📘 Full development guide →

📁 Repository Structure

├── Day_01_Introduction → Day_67_Model_Monitoring_and_Reliability/
│   └── Self-contained lessons with READMEs, scripts, and notebooks
├── docs/          # Documentation, curriculum roadmaps, guides
├── tools/         # Build scripts for docs and notebooks
├── tests/         # 233+ automated tests
└── data/          # Sample datasets

🙌 Contributing

We welcome contributions that keep the curriculum practical and accessible!

• 🐛 Report bugs via GitHub Issues
• 💡 Suggest improvements through Pull Requests
• 📖 Improve documentation - every contribution helps

📘 Read the contributing guide →

📄 License

This project is open source and available under the LICENSE file in this repository.

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