Data Science & AI Bootcamp

A Data Science & AI bootcamp is an intensive, applied training format that compresses analytics, statistics, machine learning, deep learning, model deployment, data engineering, AI tooling, and career-transition support into a project-heavy curriculum.

Key points

• Le Wagon’s syllabus defines the format as a 400-hour bootcamp covering data analytics toolkits, decision science, machine learning and deep learning, machine learning engineering, final projects, and career services ^[src-047].
• The curriculum starts with Python, SQL, Jupyter, Pandas, NumPy, visualization, and BigQuery before moving into regression, hypothesis testing, confidence intervals, and statistical consulting-style analysis ^[src-047].
• The machine learning block uses Scikit-learn, XGBoost, and LangChain for supervised learning, unsupervised learning, structured data, image/text tasks, pipelines, and model fine-tuning ^[src-047].
• The deep learning block includes TensorFlow, Keras, Hugging Face, Gemini, ChatGPT, Copilot, transformers, RAG pipelines, GenAI agents, transfer learning, recurrent networks, and LLM fine-tuning ^[src-047].
• The MLOps block teaches packaging, cloud training, Google Cloud, BigQuery, MLflow, Prefect, Docker, FastAPI, Streamlit, monitoring, retraining, and exposing predictions through APIs or web apps ^[src-047].
• The syllabus extends beyond modeling into AI ethics, explainability with tools such as SHAP, CI/CD, Agile project management, team projects, and career services ^[src-047].
• Liora’s Data Scientist syllabus is another 400-hour version of the format, delivered 100% remotely with bootcamp or part-time rhythms and about 120 hours of project work ^[src-050].
• Liora’s curriculum emphasizes Python, visualization, software tooling, classical and advanced ML, applied ML ethics and SHAP, deep learning, PyTorch, Hugging Face, LLMs/GenAI, SQL, PySpark, Docker, MLflow, and AWS Cloud Practitioner preparation ^[src-050].
• MIT Professional Education’s Applied AI and Data Science Program adds a shorter professional-education variant: 14 weeks, 12-18 hours per week, 50+ case studies, MIT faculty live sessions, industry mentorship, a capstone, and 16 CEUs ^[src-060].
• MIT’s curriculum reinforces the convergence of classical data science and GenAI: Python/statistics, supervised and unsupervised learning, deep learning, computer vision, recommendation systems, prompt engineering, RAG, and Agentic AI ^[src-060].
• Howell’s resource-roadmap video provides the self-directed version of the same curriculum: programming/software engineering, maths/statistics, machine learning, deep learning/LLMs, and AI engineering, with projects doing the real learning consolidation ^[src-075].
• The source is a useful counterweight to bootcamp shopping: choose one strong resource for the current layer, learn enough fundamentals, then build rather than accumulating random courses ^[src-075].

Related entities

• Le Wagon
• Liora
• MIT Professional Education
• Great Learning
• Egor Howell

Related concepts

• Retrieval-Augmented Generation (RAG)
• Continuous Agent Evaluation
• AI Product Experimentation
• Offline Evals to Online Experiments
• Liora Data Scientist Bootcamp
• MIT Applied AI and Data Science Program
• AI Learning Roadmap
• Project-Based AI Learning
• AI Engineering Skill Stack

Source references

• ^[src-047] Le Wagon – “Le Wagon Data Science & AI Bootcamp Syllabus” (2024)
• ^[src-050] Liora – “Liora Data Scientist Syllabus” (2026-01)
• ^[src-060] MIT Professional Education / Great Learning — “MIT Applied AI and Data Science Program Brochure” (2025-12)
• ^[src-075] Egor Howell — “STOP Taking Random AI Courses – Read These Books Instead” (2025-06-14)