Build Practical Expertise in AI, ML, GenAI, and Agentic AI

MIT Professional Education's Applied AI and Data Science Program is a 15-week online program that builds technical capability in Data Science, Machine Learning, Generative AI, and Agentic AI. Delivered through 15 live online sessions by MIT faculty, the program also includes recorded lectures, expert mentorship, 10+ case studies, hands-on projects, and a capstone project. Learners who complete the program receive a Certificate of Completion from MIT Professional Education and 16 Continuing Education Units (CEUs).

The program runs for 15 weeks and is delivered entirely online. It includes 15 live online sessions by MIT faculty, mentor-led sessions, self-paced modules, a graded elective project, and a final capstone project spanning weeks 13 through 15.

The 15-week program requires an average of 12–18 hours per week. This includes recorded lectures and live online sessions with MIT faculty, mentored learning sessions, self-study, and project work. The program includes two refresher break weeks (weeks five and nine) to allow learners to consolidate content and complete pending work.

After completing the program, you will be able to:
● Apply Python and AI coding assistants to build, debug, and evaluate code for real-world Data Science tasks.
● Design and train deep learning models, including CNNs and transfer learning pipelines, for pattern recognition and advanced prediction tasks.
● Construct single-agent and multi-agent AI systems using LangGraph, RAG, and production frameworks to solve autonomous business problems.
● Use statistical reasoning and Machine Learning techniques to analyze data, build predictive models, and rigorously evaluate performance.
● Build end-to-end AI systems for recommendation engines, time-series forecasting, and unsupervised pattern discovery.
● Evaluate and build Agentic AI workflows using key performance metrics such as tool accuracy and adaptive reasoning quality.

The 15-week curriculum is structured as follows:
● Pre-work: Python, Data Science, and AI Foundations (Python fundamentals, data science foundations, probability, and descriptive statistics)
● Week 1: AI-Assisted Python Programming (using tools like VSCode, Codex, and GPT-5)
● Week 2: AI-Assisted Statistical Analysis and Data Preparation (inferential statistics, hypothesis testing, and AI-assisted data preparation)
● Week 3: Data Analysis and Visualization (dimensionality reduction, network representation, and clustering)
● Week 4: Machine Learning (regression, classification, and model evaluation)
● Week 5: Refresher Break (catch up on coursework and consolidate learning)
● Week 6: Practical Data Science (decision trees, ensemble learning, and time series analysis)
● Week 7: Deep Learning (neural network foundations, CNNs, and transfer learning)
● Week 8: Recommendation Systems (collaborative filtering, matrix estimation, and neural approaches)
● Week 9: Refresher Break (catch up on coursework and consolidate learning)
● Week 10: Elective Project (select a problem statement from a chosen domain and apply AI techniques)
● Week 11: Generative AI and Agentic AI Foundations (autonomous agents, memory, planning, and tool use)
● Week 12: Building and Evaluating Agentic AI Workflows (multi-agent systems, adaptive RAG, and production metrics)
● Weeks 13–15: Capstone Project (design and deliver an end-to-end AI solution integrating concepts across the program)
Additionally, the program features:
● Self-paced Modules: Ethical and Responsible AI; Claude-Based AI Workflows (supported by ~5 hours of structured learning).
● Masterclass: Anthropic session covering Claude model capabilities, Constitutional AI, safety, API usage, and application development.

The program covers Python, Google Colab, Visual Studio Code, OpenAI GPT-5, n8n, Gemini, Claude, LangChain, LangGraph, and Codex. Learners also receive access to OpenAI API keys and Codex for AI-assisted coding, provided by Great Learning. The curriculum applies these tools across Data Science, Machine Learning, Generative AI, and Agentic AI use cases.

Yes. The program covers Generative AI and Agentic AI foundations, including the architecture of autonomous AI agents, single-agent workflow design, and applying agents to solve business problems. It also covers building and evaluating Agentic AI workflows, including multi-agent system design, Retrieval-Augmented Generation (RAG), dynamic task routing, and evaluation metrics such as tool accuracy and BERTScore. There is also a self-paced module on Claude-based AI workflows.

The program builds Agentic AI skills in designing and constructing single-agent and multi-agent AI systems using LangGraph, RAG, and production frameworks. Learners work on designing task-oriented agent workflows, handling uncertainty and errors in agent systems, applying adaptive RAG in multi-agent generative AI contexts, and evaluating agent performance using metrics such as tool accuracy, ROUGE, BERTScore, and LLM-as-a-Judge. A self-paced module also covers Agentic workflow design and orchestration using Claude.

The program includes 3 graded projects (1 data analysis project due in week 2, 1 elective project, and 1 capstone project), and 10+ case studies spanning industries including retail, finance, healthcare, SaaS, e-commerce, manufacturing, and HR.
Sample case studies include an employee HR policy single-agent assistant, a credit default risk predictor, and a banking customer service multi-agent copilot. The capstone project runs across weeks 13–15 and requires learners to design and deliver an end-to-end AI solution integrating concepts and techniques from across the entire program.

Yes. The program includes 9 graded quizzes focused on key concepts in Machine Learning, Deep Learning, Generative AI, and Agentic AI. These quizzes are designed to build your practical intuition and ensure you are mastering the tools and techniques as you progress. To successfully complete the program and earn your certificate, you must score at least 60% in each course.

The capstone project spans weeks 13–15 and requires learners to design and deliver an end-to-end AI solution for a chosen problem statement. It integrates techniques from across the program, including Data Analysis, Machine Learning, Deep Learning, Recommendation Systems, and Generative and Agentic AI workflows. Learners design and deliver an end-to-end AI solution for a selected problem statement, integrating concepts and techniques from across the program.

The 15 live online sessions are led by MIT faculty. Program faculty include:
● Devavrat Shah - The Andrew (1956) and Erna Viterbi Professor in the Department of Electrical Engineering and Computer Science at MIT.
● Munther Dahleh, William A. Coolidge Professor and the founding director of the Institute for Data, Systems, and Society (IDSS).
● Caroline Uhler - A faculty member at MIT with joint appointments in Electrical Engineering and Computer Science (EECS) and the Institute for Data, Systems, and Society (IDSS).
● John Tsitsiklis - Clarence J. Lebel Professor in the Department of Electrical Engineering and Computer Science at MIT and is affiliated with the Laboratory for Information and Decision Systems.
● Stefanie Jegelka - Stefanie Jegelka is an Associate Professor in the Department of Electrical Engineering and Computer Science at MIT.
Note: Program faculty names above are indicative and subject to change

Program mentors are Data Science and Machine Learning professionals from companies including Apple, Amazon, Microsoft, BlackRock, Google, Ford, AstraZeneca, and Siemens Healthineers. They deliver 8+ hours of recorded video lectures and lead live mentored learning sessions. Mentors guide learners through industry case studies and projects, providing practical context for applying concepts to real-world scenarios. Note: the mentor list is indicative and subject to change.