GRIT - Growth through Rigorous Iterative Training

Complete Data Science Learning Path: Zero to Job-Ready

A comprehensive, project-based curriculum covering Python, SQL, data analysis, and machine learning. Built for self-learners who want practical skills and a strong portfolio.

What This Is

GRIT is a structured learning path that takes you from zero coding experience to job-ready data scientist. Each module builds on the previous one, with hands-on projects and real-world applications.

Core Principles:

• Project-based learning (build your portfolio as you learn)
• Incremental progression (master fundamentals before advancing)
• Industry-focused skills (learn what employers actually need)
• Open source and completely free

Repository Structure

01-Python-Fundamentals

Master Python basics through 10 progressive notebooks. Covers variables, data structures, control flow, functions, and file handling. Each notebook includes exercises and debug challenges.

Projects: Calculator app, text parser, data cleaner

02-SQL-Mastery

Learn SQL from basics to advanced queries. Includes practical database design, joins, aggregations, and query optimization using real e-commerce data.

Projects: Business analytics dashboard, customer segmentation analysis

03-Data-Libraries

Deep dive into the Python data science stack:

• NumPy: Array operations, vectorization, linear algebra
• Pandas: Data manipulation, cleaning, transformation
• Matplotlib: Publication-quality static visualizations
• Seaborn: Statistical graphics and analysis
• Plotly: Interactive dashboards and web visualizations

Projects: Customer analytics pipeline, interactive sales dashboard, image processing system

04-Data-Science-Foundations

Mathematical foundations for data science:

• Mathematics for data science
• Statistics and probability
• Data preprocessing techniques
• Exploratory data analysis framework
• End-to-end ML pipeline

Projects: Statistical analysis toolkit, data preprocessing pipeline

05-Kaggle-Notebooks

Curated collection of 29 high-impact Kaggle notebooks covering:

• Exploratory data analysis techniques
• Feature engineering strategies
• Machine learning model development
• Deep learning applications
• NLP and text analysis
• Data visualization best practices
• Competition strategies

Each notebook is selected for teaching value and real-world applicability. Includes beginner to advanced content across multiple domains.

Learning Path

Phase 1: Foundations (2 months)

1. Python Fundamentals (10 days)
2. SQL Mastery (7 days)
3. Data Libraries (5 weeks)

Phase 2: Core Skills (1 month)

1. Data Science Foundations (2 weeks)
2. Kaggle Notebooks Study (2 weeks)

Phase 3: Advanced Topics (3+ months)

• Machine learning algorithms
• Deep learning and neural networks
• Natural language processing
• MLOps and deployment

Getting Started

# Clone the repository
git clone https://github.com/muhammadibrahim313/GRIT.git
cd GRIT

# Start with Python fundamentals
cd 01-Python-Fundamentals
jupyter notebook 01_hello_python.ipynb

Recommended approach:

• Spend 45-90 minutes daily on structured learning
• Complete exercises before moving forward
• Build the projects (essential for portfolio)
• Review and take notes regularly

Prerequisites

Required:

• Computer with internet access
• Willingness to learn and practice consistently

Helpful but not required:

• Basic computer literacy
• High school math

Tech Stack

• Python 3.8+
• Jupyter Notebooks
• pandas, numpy, matplotlib, seaborn, plotly
• scikit-learn, scipy
• SQLite

Projects You'll Build

By the end of this curriculum, you'll have built:

• 15+ data analysis projects
• Multiple ML applications
• Interactive dashboards
• SQL-based business analytics
• Data preprocessing pipelines
• Portfolio-ready notebooks

Contributing

Contributions are welcome. Please:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

Focus areas for contribution:

• Additional exercises
• Project ideas
• Bug fixes
• Documentation improvements
• Dataset additions

License

• Code: MIT License
• Educational content: CC BY-NC 4.0

Progress Tracking

Check PROGRESS.md for detailed completion status and ROADMAP.md for planned features.

Support

• Issues: Report bugs or request features via GitHub Issues
• Discussions: Share ideas and ask questions in Discussions

Acknowledgments

Built by learners, for learners. Thanks to all contributors and the open-source data science community.

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Note: This is a living curriculum. Content is regularly updated based on industry trends and community feedback.

Start your data science journey today. Every expert was once a beginner.