Skip to content

Learning Paths

Guided journeys through this repository's categories. Each path orders categories and cheat sheets into a sequence you can follow from beginner to working knowledge.

How to Use These Paths

  • Follow the categories in order — each step builds on the previous one.
  • Cheat sheets are references: skim the whole sheet once, then return to the sections you need.
  • Milestones are checkpoints. When you hit one, you have something real to show for the work.

1. Data Analyst

Goal: Import, clean, analyze, and visualize data — and answer questions with it.

  1. Python — basics, OOP, regex
  2. SQL & Databases — SQL essentials, SQLite for practice
  3. Data Analysis — pandas, NumPy
  4. Mathematics & Statistics — statistics, statistical-distributions, hypothesis-testing
  5. Data Visualization — Matplotlib, seaborn
  6. Other Tools — yaml, jq, curl — the analyst's everyday tools

Milestones: Clean a messy CSV end-to-end → answer 5 business questions with SQL + pandas → publish a 3-chart dashboard.

2. Data Scientist / ML Engineer

Goal: Build, evaluate, and interpret machine learning models.

  1. Python — basics, OOP, typing, stdlib
  2. Data Analysis — pandas, NumPy, statsmodels
  3. Mathematics & Statistics — linear algebra, probability, statistics
  4. Machine Learning — scikit-learn, model selection, evaluation
  5. Git & GitHub — git for experiments, GitHub for collaboration
  6. Data Visualization — seaborn, Matplotlib, Plotly
  7. MLOps — mlflow, model-deployment, model-monitoring

Milestones: Train and evaluate a baseline model on a real dataset → reproduce an experiment from a notebook → deploy a model behind an API.

3. Deep Learning Engineer

Goal: Train and optimize neural networks with modern frameworks.

  1. Python — basics, typing
  2. Data Analysis — NumPy fundamentals
  3. Mathematics & Statistics — calculus, linear algebra, optimization
  4. Machine Learning — scikit-learn, preprocessing, model selection
  5. Deep Learning — PyTorch / TensorFlow
  6. Hardware & GPU — CUDA and memory optimization
  7. Computer Vision — opencv, torchvision, yolo or NLP — nlp-preprocessing, spacy, huggingface-transformers — pick a domain

Milestones: Train a model on GPU from scratch → beat a baseline on a public benchmark → optimize memory and training time.

4. GenAI / LLM Engineer

Goal: Build applications powered by LLMs — from prompting to RAG to agents.

  1. Python — basics, venv, uv
  2. LLM — llm-concepts, ollama, litellm, generative-ai
  3. RAG — rag, embeddings, chunking, retrieval, faiss
  4. AI Agents — ai-agents, tools, planning, multi-agent-systems
  5. APIs — rest-api, fastapi, flask, python-requests
  6. Apps & UI — streamlit, gradio, data-app-patterns
  7. Cybersecurity — prompt injection and AI security

Milestones: Build a chatbot over your own documents → add tools to an agent → ship a RAG app with a UI.

5. Data Engineer

Goal: Build and operate the pipelines that move and transform data.

  1. Python — basics, stdlib, pip
  2. SQL & Databases — SQL, PostgreSQL, SQLite
  3. Linux — linux-commands, bash
  4. Git & GitHub — git, GitHub
  5. Docker — docker, docker-compose
  6. Data Engineering — airflow, etl-elt, data-pipelines
  7. Big Data — apache-spark, pyspark, kafka
  8. Cloud — cloud-fundamentals, aws / google-cloud — storage and compute

Milestones: Automate a weekly ETL job → run a dbt transformation with tests → process a dataset too big for one machine.

6. MLOps Engineer

Goal: Deploy, monitor, and operate ML systems reliably.

  1. Python — basics, typing, venv
  2. Git & GitHub — git, GitHub
  3. Docker — docker, docker-compose
  4. Linux — linux-commands, bash
  5. DevOps — devops, ci-cd, github-actions, kubernetes
  6. Cloud — cloud-fundamentals, aws, azure, google-cloud
  7. Machine Learning — scikit-learn — enough ML to serve it
  8. MLOps — mlops, mlflow, dvc, model-deployment

Milestones: Containerize a model → automate retraining in CI → monitor live model drift.

7. AI Application Builder

Goal: Turn AI capabilities into working products.

  1. Python — basics, venv
  2. Web Development — html, css, javascript, http, json
  3. APIs — rest-api, fastapi, flask, curl, postman
  4. Apps & UI — streamlit, gradio, data-app-patterns
  5. LLM — openai-api, anthropic-api, huggingface
  6. RAG — rag, retrieval, reranking, rag-evaluation
  7. Testing & Debugging — ship with confidence

Milestones: Ship a single-page AI app → add authentication and persistence → launch something other people use.

Found a gap in a path or want a new one? Open an issue — see CONTRIBUTING.md.