MIT Applied AI and Data Science Program

The MIT Applied AI and Data Science Program is a 14-week live online professional-education program offered by Mit Professional Education with Great Learning, covering Python, statistics, machine learning, deep learning, recommendation systems, computer vision, Generative AI, RAG, Agentic AI, and a final capstone [src-060].

Key points

  • The brochure describes the program as an upgraded version of the earlier Applied Data Science Program, with 8,000+ alumni, 70+ batches, a 4.4/5 program rating, 98% satisfaction, and 92% completion rate for the prior program [src-060].
  • The 14-week structure includes 2 weeks of Python/statistics foundations, 8 weeks of core curriculum and practical applications, 1 week for project submissions, 3 weeks for a capstone, and self-paced ethical/responsible AI [src-060].
  • The curriculum spans Python, NumPy, Pandas, Seaborn, Matplotlib, Plotly, inferential statistics, clustering, PCA/t-SNE, regression, causal inference, cross-validation, decision trees, random forests, deep learning, CNNs, transfer learning, recommendation systems, prompt engineering, RAG, and Agentic AI [src-060].
  • The program uses a low-code/applied framing rather than a pure engineering bootcamp, emphasizing case studies, business applications, and decision-making for working professionals [src-060].
  • Case studies and capstones include brain-tumor image classification, hotel cancellation prediction, stock portfolio optimization, movie/music recommendation, marketing segmentation, used-car price prediction, loan default prediction, malaria detection, facial emotion detection, and GenAI-powered customer-review categorization [src-060].
  • Completion requires at least 60% in each course, including the elective and capstone, and awards a Certificate of Completion plus 16.0 CEUs [src-060].

Related entities

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Source references

  • [src-060] MIT Professional Education / Great Learning — "MIT Applied AI and Data Science Program Brochure" (2025-12)

Robin Cartier perspective

This page is part of Robin Cartier's working AI knowledge graph: a practical research layer for production AI, recommendation systems, experimentation, GEO, and agentic web readiness.

The useful next step is to connect this concept back to applied product leadership and operating models.

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