Program Overview

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Applied AI and Data Science Program

The Applied AI and Data Science Program, offered by MIT Professional Education, aims to prepare decision-makers for the future by helping them unravel the true worth of data. This program is delivered in collaboration with Great Learning and features a curriculum developed and taught by MIT faculty.

Program Overview

In this 14-week program, participants will upgrade their artificial intelligence and data science skills by learning the theory and practical application of various concepts, including prompt engineering, agentic AI, ethical AI, supervised and unsupervised learning, time-series analysis, neural networks, recommendation engines, regression, and computer vision.

Upon successful fulfillment of requirements, participants will receive a certificate of completion from MIT Professional Education at the end of the program.

Program Mentors

The program mentors are experienced data scientists and machine learning experts from top organizations, including Apple, Microsoft, BlackRock, and Amazon. They will coach participants to work on hands-on industry-relevant projects, providing personalized mentoring and learning sessions to give participants a practical understanding of core concepts.

• Omar Attia - Senior Research Engineer, Apple
  • Bhaskarjit Sarmah - Director, BlackRock
  • Vibhor Kaushik - Senior Machine Learning Scientist, Amazon
  • Nirmal Budhathoki - Senior Data Scientist, Microsoft
  • Mohit Khakaria - Senior Machine Learning Engineer, Ford Motor Company
  • Rohit Dixit - Senior Data Scientist, Siemens Healthineers
  • Vaibhav Verdhan - Senior Director Global, AstraZeneca
  • Udit Mehrotra - Data Scientist, Google
  • Amish Suchak - Data Science Team Lead, XSOLIS
  • Nirupam Sharma - Data Science Vice President, Big Village
  • Deepa Krishnamurthy - Director, AI Solutions Engineering, Koru
  • Marco De Virgilis - Actuarial Data Scientist Manager, Arch Insurance Group Inc.
  • Cristiano Santos De Aguiar - Biomedical Machine Learning Engineer, Oncoustics
  • Matt Nickens - Senior Manager, Data Science, CarMax
  • Saber Fallahpour - Principal Data Scientist, Altair
  • Asim Sultan - Senior Machine Learning Engineer, RiskHorizon AI

Program Curriculum

The program is 14 weeks long, consisting of:

• 2 weeks for foundations
• 8 weeks of core curriculum, including practical applications
• 1 week for project submissions
• 3 weeks for a final, integrative Capstone project

Week 1&2 - Foundations of AI

• Python for Data Science (NumPy & Pandas)
• Python for Visualization
• Inferential Statistics
• Hypothesis Testing

Week 3 - Data Analysis & Visualization

• Hypothesis testing and practical applications
• Dimensionality reduction using PCA and t-SNE
• Network Analysis
• Different types of clustering algorithms

Week 4 - Machine Learning

• Maximum Likelihood, Bayesian Estimators & formulation
• Linear Regression & Assumptions
• Cross-validation & Bootstrapping
• Classification using Logistic Regression & KNN)
• Gaussian Models

Week 5 - Revision Break

Week 6 - Practical Data Science

• Introduction to Decision Tree
• Entropy & Information Gain
• Ensemble Learning - Bagging, Bootstrapping, and Random Forests
• Time Series Forecasting

Week 7 - Deep learning

• Introduction to Deep Learning
• Filters/Convolutions, Pooling, and Max-Pooling
• Architecture of CNN
• Transfer Learning and Augmentation
• Encoder Decoder Architecture
• Token-based Processing, Attention Mechanism & Positional Encodings

Week 8 - Recommendation Systems

• Introduction to the Recommendations
• Content-Based Recommendation Systems
• Collaborative Filtering & Singular Value Thresholding
• Matrix Estimation Meets Content-Based
• Matrix Estimation Over Time

Week 9 - Project Week

Time for participants to finish and submit their projects

Week 10 - Generative AI Foundations

• Origins of Generating New Data
• Generative AI as a Matrix Estimation Problem
• LLM as a Probabilistic Model for Sequence Completion
• Prompt Engineering

Week 11 - Business Applications of Generative AI

• Natural Language Tasks with Generative AI
  • Summarization, Classification and Generation
  • Retrieval Augmented Generation (RAG)
  • Agentic AI

Week 12-14 - Capstone Project

Learning Outcomes

• Explore the core concepts behind Artificial Intelligence, Generative AI, and Data Science, and their real-world applications.
• Learn how to transform and structure data to build more accurate and reliable Machine Learning models.
• Use a range of techniques to solve data-driven challenges and support decision-making across business functions.
• Explore how different modern AI techniques can be implemented across various business applications.
• Complete a portfolio of hands-on projects, including a 3-week Capstone, that showcases your ability to apply Data Science techniques to meaningful business scenarios.

Who Should Attend

• Professionals who are interested in a career in Data Science and Machine Learning.
• Professionals interested in leading Data Science and Machine Learning initiatives at their companies.
• Entrepreneurs interested in innovation using Data Science and Machine Learning.

Prerequisites

Basic knowledge of Computer Programming and Statistics

Testimonials

The program has received positive feedback from past participants, who have praised the program's structure, supportive learning materials, and great lectures. Participants have reported gaining a solid foundation in fundamental ML algorithms and feeling confident to take on real-life projects.

Program Details

• Date(s): Nov 15, 2025 - Mar 22, 2026
• Location: Live Online
• Course Length: 14 Weeks
• Course Fee: $3,900
• CEUs: 16 CEUs