A comprehensive AI and data science curriculum covering NumPy, Pandas, Prompting, and LLMs. Everything from basics to advanced techniques for individuals and teams.
This playbook is designed as a structured learning path for developers, data scientists, and AI practitioners to master essential AI and data science tools. Each playbook builds upon fundamental concepts, providing both theoretical knowledge and practical examples.
By the end of this playbook collection, you will:
- Master NumPy for numerical computing and array operations
- Excel at Pandas for data manipulation and analysis
- Become proficient in prompt engineering and AI interaction
- Understand Large Language Models (LLMs) and their applications
- Apply best practices across all AI and data science workflows
11 modules covering array operations, linear algebra, statistics, and performance optimization.
Topics:
- Array creation and manipulation
- Advanced indexing and slicing
- Linear algebra operations
- Statistics and probability
- Performance optimization
- Integration with other libraries
Time Investment: 15-20 hours (beginner) | 8-12 hours (experienced)
11 modules covering data manipulation, analysis, time series, and visualization.
Topics:
- Data structures (Series, DataFrame)
- Data cleaning and transformation
- GroupBy and aggregations
- Merging and joining datasets
- Time series analysis
- Data visualization
- Performance optimization
Time Investment: 20-25 hours (beginner) | 10-15 hours (experienced)
5 modules covering prompt engineering, model-specific techniques, and special frameworks.
Topics:
- Basic and advanced prompting styles
- Model-specific optimizations (GPT-4o, Claude, Gemini, etc.)
- Special frameworks (Chain of Thought, ROSE, Few-shot, etc.)
- Best practices and common pitfalls
Time Investment: 10-15 hours (beginner) | 5-8 hours (experienced)
8 modules covering Large Language Models from fundamentals to practical applications.
Topics:
- Introduction to LLMs
- How LLMs work
- Types of LLMs
- Use cases and limitations
- Getting started
- Best practices
- Common misconceptions
Time Investment: 8-12 hours (beginner) | 3-4 hours (experienced)
This playbook collection can be used in several ways depending on your experience level, goals, and available time:
For someone new to AI and data science:
Phase 1: Foundations (20-30 hours)
- Start with LLM Playbook - 8-12 hours
- Understand what AI and LLMs are
- Learn how to interact with AI systems
- Then Prompting Playbook - 10-15 hours
- Learn effective prompting techniques
- Master AI interaction
Phase 2: Data Science (35-45 hours)
- NumPy Playbook - 15-20 hours
- Master numerical computing fundamentals
- Pandas Playbook - 20-25 hours
- Learn data manipulation and analysis
Time commitment: 2-3 hours per module, work through sequentially
For developers with programming experience:
Quick Start (8-12 hours)
- LLM Playbook - 3-4 hours (skip basics, focus on practical)
- Prompting Playbook - 5-8 hours (focus on advanced techniques)
Data Science (18-27 hours)
- NumPy Playbook - 8-12 hours (start with Module 3, focus on advanced)
- Pandas Playbook - 10-15 hours (start with Module 3, focus on advanced)
Time commitment: 1-2 hours per module, jump to relevant sections
For data scientists focusing on data manipulation:
Core Skills (30-40 hours)
- NumPy Playbook - 15-20 hours (complete)
- Pandas Playbook - 20-25 hours (complete)
AI Enhancement (5-10 hours)
- LLM Playbook - 3-4 hours (quick overview)
- Prompting Playbook - 5-8 hours (focus on data analysis prompts)
Time commitment: Focus on data science tools, add AI skills for enhancement
For AI/ML engineers focusing on AI interaction:
AI Skills (13-19 hours)
- LLM Playbook - 8-12 hours (complete)
- Prompting Playbook - 5-8 hours (complete)
Data Tools (5-8 hours)
- NumPy Playbook - 3-4 hours (essentials only)
- Pandas Playbook - 2-4 hours (essentials only)
Time commitment: Focus on AI, add data tools as needed
For teams adopting AI and data science:
Week 1: AI Fundamentals (5-7 hours)
- All team members: LLM Playbook Modules 1, 2, 6 - 3-4 hours
- All team members: Prompting Playbook Modules 1, 2 - 2-3 hours
Week 2: Data Tools (5-7 hours)
- Data team: NumPy Playbook Modules 1-3 - 3-4 hours
- Data team: Pandas Playbook Modules 1-3 - 2-3 hours
Week 3: Advanced Topics (5-6 hours)
- Assign based on roles:
- Developers: Advanced prompting, model-specific guides
- Data Scientists: Advanced NumPy/Pandas
- Analysts: Pandas focus, basic prompting
Time commitment: 1 hour per module + team discussions
For experienced users:
- Bookmark individual playbook reference sections
- Use specific modules to answer questions
- Refer to model-specific guides as needed
- Use cheat sheets and troubleshooting guides
-
Start Here: LLM Playbook
- Understand what AI is
- Learn to interact with AI systems
- Build confidence with AI tools
-
Then: Prompting Playbook
- Learn effective AI interaction
- Master prompt engineering
- Optimize for different models
-
Next: NumPy Playbook
- Master numerical computing
- Learn array operations
- Build foundation for data science
-
Finally: Pandas Playbook
- Learn data manipulation
- Master data analysis
- Complete your data science toolkit
- Quick AI Overview: LLM Playbook + Prompting Playbook
- Data Tools: NumPy Playbook + Pandas Playbook
- Focus on: Advanced modules and optimization
- Core Tools: NumPy Playbook + Pandas Playbook
- AI Enhancement: Prompting Playbook (for AI-assisted analysis)
- Understanding: LLM Playbook (optional, for context)
| Playbook | Modules | Focus | Best For | Time (Beginner) |
|---|---|---|---|---|
| LLM | 8 | Understanding AI | Everyone | 8-12 hours |
| Prompting | 5 | AI Interaction | AI Users | 10-15 hours |
| NumPy | 11 | Numerical Computing | Data Scientists | 15-20 hours |
| Pandas | 11 | Data Analysis | Data Analysts | 20-25 hours |
- Complete Beginner: Start with LLM Playbook
- Developer: Start with Prompting Playbook
- Data Scientist: Start with NumPy Playbook
- Analyst: Start with Pandas Playbook
Select a learning path above that matches your:
- Experience level
- Time availability
- Career goals
- Current needs
- Begin with the first module of your chosen playbook
- Complete exercises in each module
- Practice with real-world examples
- Move to next playbook when ready
- Apply concepts to real projects
- Build a portfolio
- Share learnings with others
- Continue to advanced topics
Each playbook includes:
- Quick Reference Guides: Fast lookups
- Cheat Sheets: Common commands and patterns
- Troubleshooting: Solutions to common issues
- Exercises: Hands-on practice
- Examples: Real-world use cases
This playbook collection is designed to be:
- Comprehensive: Cover all essential AI and data science skills
- Scalable: Suitable for teams of any size
- Flexible: Mix and match based on team needs
- Practical: Focus on real-world applications
- Progressive: Build skills incrementally
-
Gradual Adoption:
- Start with one playbook (e.g., Prompting for AI teams)
- Implement practices incrementally
- Expand to other playbooks as needed
-
Full Training Program:
- Complete all playbooks sequentially
- Establish team standards
- Create internal best practices
- Regular refresher sessions
-
Role-Based Training:
- Data Scientists: NumPy + Pandas
- AI Engineers: LLM + Prompting
- Analysts: Pandas + Basic Prompting
- Everyone: LLM basics
-
Reference Implementation:
- Use as reference for specific topics
- Adopt best practices selectively
- Integrate into existing workflows
- Customize for your organization
- 11 Modules: From introduction to integration
- Focus: Numerical computing and array operations
- Prerequisites: Basic Python knowledge
- Outcome: Master numerical computing with NumPy
- 11 Modules: From basics to advanced data analysis
- Focus: Data manipulation and analysis
- Prerequisites: Basic Python, NumPy helpful
- Outcome: Excel at data analysis with Pandas
- 5 Modules: From basics to model-specific optimization
- Focus: Effective AI interaction and prompt engineering
- Prerequisites: Basic AI/LLM awareness
- Outcome: Master prompt engineering for all major models
- 8 Modules: From fundamentals to practical applications
- Focus: Understanding and using Large Language Models
- Prerequisites: None (beginner-friendly)
- Outcome: Understand LLMs and use them effectively
- Practice Regularly: Apply concepts immediately
- Build Projects: Create real-world applications
- Join Communities: Learn from others
- Stay Updated: AI field evolves quickly
- Teach Others: Reinforce your learning
- Individual Learning: Set aside 2-3 hours weekly per module
- Pair Learning: Work with a colleague, discuss concepts
- Team Workshops: Weekly sessions to discuss modules
- Project-Based: Apply learnings to real projects
- Interactive: Use Jupyter notebooks for hands-on practice
- NumPy: NumPy Official Tutorials
- Pandas: 10 Minutes to Pandas
- Prompting: Prompt Engineering Guide
- LLMs: Hugging Face Course
- Stack Overflow (NumPy, Pandas tags)
- Reddit (r/MachineLearning, r/datascience)
- Discord servers (AI/ML communities)
- GitHub Discussions
- Assess your experience level
- Choose appropriate learning path
- Set up development environment
- Start with first playbook
- Complete exercises
- Practice with real projects
- Move to next playbook
- Build portfolio
- Share knowledge
We welcome contributions to improve these playbooks! Each playbook may have its own contributing guidelines. General contributions can include:
- Improvements to existing content
- New examples and exercises
- Corrections and updates
- Additional resources
These playbooks are open source and available for educational use. Please refer to individual playbooks for specific licensing information.
New to AI? → Start with LLM Playbook
Want to master AI interaction? → Start with Prompting Playbook
Need data science skills? → Start with NumPy Playbook
Focusing on data analysis? → Start with Pandas Playbook
Ready to begin? Choose your path above and start your learning journey! 🚀