Data Science & AI Cheat Sheets¶
154 concise, practical, community-maintained cheat sheets for the entire data science and AI stack — from Python and pandas to machine learning, LLMs, RAG, and AI agents. One page per tool. No fluff.
Table of Contents¶
- Introduction
- Why This Repository Exists
- Quick Start
- Cheat Sheet Finder
- Categories
- Technology Index
- Learning Paths
- Repository Structure
- How to Contribute
- Roadmap
- GitHub Pages
- License
Introduction¶
What is this? A single, organized reference for the tools and technologies used across the modern data science and AI stack. Instead of re-reading long tutorials or digging through documentation, you get the essentials on one page: syntax, commands, patterns, and gotchas.
Who is it for?
- Data analysts and scientists who need quick syntax and pattern references.
- Machine learning and deep learning engineers building and training models.
- GenAI / LLM developers working with prompts, RAG, and AI agents.
- Data and platform engineers who ship and operate data pipelines.
- Students and career switchers following the guided learning paths.
- Anyone who prefers a one-page reference over hunting through documentation.
Every cheat sheet is:
- Concise — the essentials on one page, no fluff.
- Practical — real commands, code snippets, and patterns you can copy.
- Accurate — verified against official documentation.
- Consistent — every sheet follows the same template, so structure stays familiar across categories.
Why This Repository Exists¶
Documentation is thorough but scattered: pandas has one site, Docker another, PyTorch a third — and LLM tooling changes weekly. When you need "how do I filter a DataFrame with two conditions?" or "what flag did kubectl need again?", you want the answer in seconds, not after ten minutes of tabs.
This repository puts the day-to-day essentials of the entire stack in one place: 30 categories, 154 cheat sheets, organized so you can browse by topic, search by tool, or follow a guided path from beginner to working knowledge.
Quick Start¶
How do I use it?
- Know the tool? Jump straight to it via the Cheat Sheet Finder or the alphabetical Technology Index below.
- Browse by category? Pick a numbered directory from the Categories table — each category page indexes its own sheets.
- New to the field? Follow a Learning Path — ordered categories with milestones, from beginner to job-ready.
- Search the repo? Use GitHub search with
in:path(e.g.in:path pandas), orCtrl/Cmd + Fon this page.
Looking for something specific?
| You want… | Go to |
|---|---|
| Python | 01-Python — syntax, OOP, stdlib, regex, typing, pip, uv |
| Pandas / NumPy | 02-Data-Analysis — data wrangling essentials |
| Machine Learning | 04-Machine-Learning — scikit-learn, evaluation, tuning |
| LLMs / RAG / Agents | 08-LLM · 09-RAG · 10-AI-Agents |
| Docker / Linux / SQL | 17-Docker · 18-Linux · 12-SQL-Databases |
| Learn systematically | LEARNING-PATHS.md — guided journeys with milestones |
The master navigation index lists every category, document, and cheat sheet in one place.
Cheat Sheet Finder¶
Looking for a topic but not sure where it lives? Find its home category here.
| Tool / Topic | Category |
|---|---|
| venv, pip, uv, packaging, environments | Python |
| OOP, typing, regex, standard library | Python |
| pandas, NumPy, data wrangling | Data Analysis |
| Polars, lazy DataFrames | Data Analysis |
| SciPy, statsmodels, hypothesis tests, regression | Data Analysis |
| Matplotlib, seaborn, static charts | Data Visualization |
| Plotly, Bokeh, Altair, interactive charts | Data Visualization |
| folium, GeoPandas, maps, geospatial | Data Visualization |
| scikit-learn, pipelines, preprocessing | Machine Learning |
| Cross-validation, tuning, evaluation metrics | Machine Learning |
| Clustering, PCA, feature engineering | Machine Learning |
| PyTorch, TensorFlow, Keras | Deep Learning |
| NLTK, tokenization, stemming, lemmatization | NLP |
| spaCy, NER, dependency parsing | NLP |
| Hugging Face transformers, fine-tuning | NLP |
| Semantic search, sentence-transformers | NLP |
| GPT-4o, Claude, Gemini APIs | Generative AI |
| Embeddings, context, temperature | Generative AI |
| LangChain, chains, prompts, tools | Generative AI |
| LangGraph, stateful workflows, agents | Generative AI |
| LlamaIndex, RAG, indexing, retrieval | Generative AI |
| LLM concepts, tokens, prompting | LLM |
| Ollama, local LLMs | LLM |
| Hugging Face, vLLM, inference | LLM |
| LiteLLM, multi-provider, proxy | LLM |
| RAG pipeline, chunking, retrieval | RAG |
| FAISS, Chroma, Qdrant, Pinecone | RAG |
| RAG evaluation, faithfulness, RAGAS | RAG |
| Agent loop, planning, routing | AI Agents |
| Tools, tool calling, execution | AI Agents |
| Multi-agent, supervisor, handoffs | AI Agents |
| OpenCV, image processing, contours, video | Computer Vision |
| YOLO, Ultralytics, detection, segmentation | Computer Vision |
| torchvision, pretrained models, fine-tuning | Computer Vision |
| SQL, window functions, CTEs, JOINs | SQL & Databases |
| PostgreSQL, MySQL, SQLite | SQL & Databases |
| MongoDB, Redis, NoSQL | SQL & Databases |
| Spark, PySpark, Kafka, Delta Lake, Databricks | Big Data |
| Airflow, ETL/ELT, data pipelines | Data Engineering |
| MLflow, DVC, model deployment, monitoring, versioning | MLOps |
| GitHub Actions, CI/CD, env vars | DevOps |
| Kubernetes, kubectl, pods, deployments | DevOps |
| Docker, Dockerfiles, Compose | Docker |
| Bash, shell scripting, Linux commands | Linux |
| PowerShell, WSL, Windows CMD | Windows |
| Git commands, branching, merge/rebase, stash | Git & GitHub |
| Pull requests, issues, Actions, GitHub Pages | Git & GitHub |
| Cloud fundamentals, IAM, regions | Cloud |
| AWS, S3, EC2, SageMaker | Cloud |
| Azure, Blob, Azure ML, Functions | Cloud |
| GCP, BigQuery, Vertex AI | Cloud |
| HTML, CSS, JavaScript, DOM, fetch | Web Development |
| HTTP methods, status codes, JSON, APIs | Web Development |
| REST design, methods, status codes | APIs |
| FastAPI, Flask, serving ML models | APIs |
| requests, curl, Postman, API testing | APIs |
| Streamlit dashboards, widgets, state | Apps & UI |
| Gradio demos, Blocks, model UIs | Apps & UI |
| VS Code, Python IDE workflows | IDEs & Editors |
| Jupyter, JupyterLab, Colab, notebooks | IDEs & Editors |
| pytest, pdb, profiling | Testing & Debugging |
| Secure coding, AI security | Cybersecurity |
| Linear algebra, matrices, SVD, eigenvalues | Mathematics & Statistics |
| Calculus, derivatives, gradients, optimization | Mathematics & Statistics |
| Probability, Bayes, distributions, hypothesis tests | Mathematics & Statistics |
| CUDA, GPU computing | Hardware & GPU |
| dotenv, .env files, environment config | Other Tools |
| YAML, TOML, config formats | Other Tools |
| make, Makefiles, task automation | Other Tools |
| SSH, keys, tunnels, scp/sftp | Other Tools |
| wget, HTTP downloads, mirrors | Other Tools |
| jq, JSON processing | Other Tools |
| curl, HTTP from the CLI | APIs |
Categories¶
The repository is organized into 30 numbered categories. Each links to its category page, which indexes every cheat sheet in it.
| # | Category | What's inside |
|---|---|---|
| 01 | Python | Syntax, OOP, stdlib, regex, typing, venvs, pip, uv — 8 sheets |
| 02 | Data Analysis | pandas, NumPy, Polars, SciPy, statsmodels — 5 sheets |
| 03 | Data Visualization | Matplotlib, seaborn, Plotly, Bokeh, Altair, folium, GeoPandas — 7 sheets |
| 04 | Machine Learning | scikit-learn, preprocessing, evaluation, feature engineering — 7 sheets |
| 05 | Deep Learning | PyTorch, TensorFlow / Keras, training and optimization (planned) |
| 06 | NLP | NLTK, spaCy, Hugging Face transformers, sentence-transformers — 7 sheets |
| 07 | Generative AI | OpenAI, Anthropic, Gemini, LangChain, LangGraph, LlamaIndex — 7 sheets |
| 08 | LLM | LLM concepts, Ollama, Hugging Face, LiteLLM — 4 sheets |
| 09 | RAG | RAG pipeline, embeddings, chunking, retrieval, vector DBs, evaluation — 12 sheets |
| 10 | AI Agents | Agent loop, tools, tool-calling, memory, planning, multi-agent — 8 sheets |
| 11 | Computer Vision | OpenCV, Pillow, torchvision, YOLO, Ultralytics — 5 sheets |
| 12 | SQL & Databases | SQL, PostgreSQL, MySQL, SQLite, MongoDB, Redis — 6 sheets |
| 13 | Big Data | Spark, PySpark, Kafka, Delta Lake, Databricks — 5 sheets |
| 14 | Data Engineering | Airflow, ETL/ELT, data pipelines — 3 sheets |
| 15 | MLOps | MLflow, DVC, deployment, monitoring, versioning — 6 sheets |
| 16 | DevOps | DevOps, CI/CD, GitHub Actions, env config, Kubernetes — 5 sheets |
| 17 | Docker | Docker & Docker Compose — 2 sheets |
| 18 | Linux | Linux commands, Bash scripting — 2 sheets |
| 19 | Windows | CMD, PowerShell, WSL — 3 sheets |
| 20 | Git & GitHub | Version control, GitHub collaboration — 2 sheets |
| 21 | Cloud | Cloud fundamentals, AWS, Azure, GCP — 4 sheets |
| 22 | Web Development | HTML, CSS, JavaScript, HTTP, JSON — 5 sheets |
| 23 | APIs | REST, requests, FastAPI, Flask, curl, Postman — 6 sheets |
| 24 | Apps & UI | Streamlit, Gradio, data app patterns — 3 sheets |
| 25 | IDEs & Editors | VS Code, Jupyter, JupyterLab, Colab — 4 sheets |
| 26 | Testing & Debugging | pytest, debugging, logging — 4 sheets |
| 27 | Cybersecurity | Security fundamentals, Linux, network, OWASP, cryptography — 5 sheets |
| 28 | Mathematics & Statistics | Linear algebra, calculus, probability, statistics, optimization — 7 sheets |
| 29 | Hardware & GPU | GPU basics, CUDA, troubleshooting, PyTorch GPU, LLM hardware — 5 sheets |
| 30 | Other Tools | dotenv, YAML, TOML, make, SSH, wget, jq — 7 sheets |
Technology Index¶
Straight to the sheet — an alphabetical index of every technology with a dedicated cheat sheet.
For the complete map of every category, document, and planned sheet, see NAVIGATION.md.
Learning Paths¶
Not sure where to start? The learning paths sequence categories and cheat sheets into guided journeys, each with milestones so you know when you've actually leveled up:
- Data Analyst — Python → SQL → pandas → statistics → visualization
- Data Scientist / ML Engineer — Python → stats → scikit-learn → MLOps
- Deep Learning Engineer — math → ML core → GPU/hardware → CV or NLP
- GenAI / LLM Engineer — LLM concepts → RAG → agents → APIs → apps
- Data Engineer — SQL → Linux → Docker → Airflow → Spark → cloud
- MLOps Engineer — Git → Docker → DevOps → cloud → deployment → monitoring
- AI Application Builder — web → APIs → LLMs → RAG → apps → testing
Each path lists the key cheat sheets to master in order, plus milestones to track progress.
Repository Structure¶
Data-Science-AI-Cheat-Sheets/
├── 01-Python/ … 30-Other-Tools/ # 30 numbered categories
│ ├── README.md # category index
│ └── <tool>.md # one cheat sheet per tool
├── templates/
│ └── CHEATSHEET_TEMPLATE.md # structure every sheet follows
├── README.md # you are here
├── NAVIGATION.md # master index of everything
├── LEARNING-PATHS.md # guided learning journeys
├── CONTRIBUTING.md # how to add a cheat sheet
├── ROADMAP.md # phased project plan
└── CHANGELOG.md # project history
How to Contribute¶
Contributions of all kinds are welcome — new cheat sheets, corrections, examples, and reviews:
- Read CONTRIBUTING.md for the full process and content guidelines.
- Copy the cheat sheet template for new entries.
- Check the roadmap and open an issue for larger ideas first.
Small fixes (typos, broken links, outdated commands) are always appreciated and don't need prior discussion.
Roadmap¶
The project evolves in phases — from foundation, to core Python and data sheets, to the GenAI stack, and finally to engineering and operations. See ROADMAP.md for the full plan, completed items, and open work.
GitHub Pages¶
This repository publishes a searchable documentation website built from the same Markdown that powers this README — every cheat sheet is available online with full-text search, syntax highlighting, and responsive navigation.
- How it is deployed: a GitHub Actions workflow (
.github/workflows/deploy-pages.yml) builds the site with MkDocs + Material for MkDocs and publishes it via the official GitHub Pages Actions (configure-pages→upload-pages-artifact→deploy-pages). - Which branch triggers deployment: pushes to
main(and manual runs from the Actions tab). - Where the published site is available:
📚 https://YOUR-USERNAME.github.io/Data-Science-AI-Cheat-Sheets/
- One-time repository setting: open Settings → Pages and set Source to GitHub Actions. Without this, GitHub serves the default branch's
README.mdinstead of the deployed site.
License¶
This repository does not yet ship a license file, so standard copyright applies to its content by default. If you'd like to see a permissive license (for example MIT or CC-BY) adopted, open an issue to discuss it — see CONTRIBUTING.md.
Maintained by the community. Found a mistake? Open an issue or submit a pull request — see CONTRIBUTING.md.