Implement AI for Real Business Impact

Applied AI and Data Science Program

Application closes on 21st May 2026

Delivered in collaboration with Great Learning

Learn more about the course

Get details on syllabus, projects, tools, and more

Name

Mobile Number

By submitting this form, you consent to our Terms of Use & Privacy Policy and consent to be contacted via email, phone (including by AI-generated/pre-recorded voice calls), SMS, or WhatsApp.

Applied AI and Data Science Program

Application closes 21st May 2026

Unlock real-world impact

Elevate your career in AI and data science

Build proficiency in advanced topics like Agentic AI, LLM orchestration, and RAG

Apply Python and AI coding assistants to build, debug, and evaluate code for real-world data science tasks
Use statistical reasoning and ML techniques to analyze data, build predictive models, and evaluate performance
Design deep learning models, including CNNs and transfer learning pipelines for advanced prediction tasks
Build AI systems for recommendation engines, time-series forecasting, and unsupervised pattern discovery
Build single- and multi-agent systems using LangGraph, RAG, and production frameworks for business challenges
Evaluate and deploy Agentic AI workflows using key performance metrics

Earn a certificate of completion from MIT Professional Education

#1 in World Universities

QS World University Rankings, 2025
#1 in AI and Data Science

QS World University Rankings by Subject, 2025
#2 in National Universities

U.S. News & World Report Rankings, 2024-2025

KEY PROGRAM HIGHLIGHTS

Why choose this program

Live online sessions with MIT faculty

Learn through recorded lectures and engage in live online sessions with renowned MIT faculty for interactive insights.
Agentic AI-infused curriculum

covers the latest in Agentic and Generative AI, including Transformers, RAG, Prompt Engineering, and modern AI frameworks with a focus on real-world business applications.
Latest AI tech stack

Explore the latest Generative AI models, including Agentic AI, Prompt Engineering and RAG modules.
Personalized mentorship by experts

Benefit from weekly online mentorship from Data Science and AI industry experts.
Build an industry portfolio

Work on 10+ case studies, projects, and a capstone project solving real business problems with AI using OpenAI keys and Codex for hands-on AI coding practice.
Personalized mentorship by experts

Earn 16 CEUs and a certificate of completion from MIT Professional Education upon completion.

Skills you will learn

Agentic AI

Prompt Engineering

Retrieval-Augmented Generation (RAG)

Multi-Agent Systems

LLM Orchestration

Prompt Optimization

AI-Assisted Coding

LLM Evaluation

AI Workflow Design

Generative AI Applications

Data Science

Generative AI

Machine Learning

Data Analysis

Deep Learning

Recommendation Systems

Ethical and Responsible AI

Agentic AI

Prompt Engineering

Retrieval-Augmented Generation (RAG)

Multi-Agent Systems

LLM Orchestration

Prompt Optimization

AI-Assisted Coding

LLM Evaluation

AI Workflow Design

Generative AI Applications

Data Science

Generative AI

Machine Learning

Data Analysis

Deep Learning

Recommendation Systems

Ethical and Responsible AI

This program is ideal for

Working professionals looking to implement AI for business impact or transition into AI and Data Science roles

Senior Technology Professionals

Ready to move beyond experimenting with AI toward designing and deploying production-grade AI systems and multi-agent workflows.
Early-Career Professionals

Experimenting with GenAI tools who want to build a rigorous technical foundation in Data Science, Machine Learning, and Agentic AI systems.
Career Transitioners

Seeking expertise in Python, Machine Learning, Agentic AI systems, and modern AI frameworks and tools such as LangChain, LangGraph, Claude, and n8n.

Comprehensive curriculum

Designed by MIT faculty, this program offers learners a complete architectural journey from classical predictive modeling to multi-agent system orchestration, equipping leaders with the critical technical intuition and strategic judgment necessary to build reliable, data-grounded solutions.

Masterclass

On Anthropic
Live Online

Sessions by MIT Faculty
10+

Emerging Tools

Pre-Work: Python, Data Science, and AI Foundations

Establish the coding and conceptual foundations needed for the program.

Concepts Covered

- Introduction to Google Colab and Python Fundamentals: Variables, Data Structures, Conditionals, and Loops - Origins and Paradigms of Data Science and AI - Probability and Descriptive Statistics - The Data Science Lifecycle: From Data Gathering to Deployment - Industry Applications of Data Science and AI Across Retail, Healthcare, and Banking

Week 1: AI-Assisted Python Programming

Use AI coding tools to accelerate Python development while evaluating generated code critically.

Concepts Covered

- Introduction to AI-Assisted Development: GitHub Copilot, ChatGPT, and Similar Tools - Strengths and Limitations of AI-Generated Code - Python Development with AI Support: Scripting, Data Handling, Pandas, and File Operations - Debugging and Validating AI-Generated Code: Unit Testing and Performance Considerations - Structured Prompting for Code Generation and Iterative Refinement

Week 2: AI-Assisted Statistical Analysis and Data Preparation

Apply inferential statistics and AI tools to draw defensible conclusions from sample data.

Concepts Covered

- Inferential Statistics: Probability Distributions, Central Limit Theorem, and Estimation Techniques - Hypothesis Testing with Confidence Intervals - Data Quality: Missing Value Treatment, Outlier Handling, Univariate and Bivariate Analysis - AI-Assisted Statistical Workflows: Using AI Tools for Hypothesis Testing and Data Preparation

Week 3: Data Analysis and Visualization (Live)

Apply dimensionality reduction and clustering techniques to uncover patterns in high-dimensional data.

Concepts Covered

- Exploratory Data Analysis with PCA, MDS, and t-SNE - Network Representation: Adjacency Matrices, Edge Density, and Degree Distribution - Centrality Measures: Degree, Eigenvector, Closeness, and Betweenness - Clustering Techniques: K-Means, Gaussian Mixture Models, Hierarchical Clustering, and DBSCAN - Network Clustering with the Louvain Method and Modularity Maximization

Week 4: Machine Learning (Live)

Build and rigorously evaluate supervised Machine Learning models for regression and classification.

Concepts Covered

- Linear Regression: Assumptions, Maximum Likelihood, and Bayesian Estimators - Model Evaluation: Overfitting, Regularization, and the Bias-Variance Tradeoff - Cross-Validation and Bootstrapping - Classification Techniques: Logistic Regression, K-Nearest Neighbors, and Bayesian Methods - Performance Assessment for Classification Models

Week 5: Revision Break

A dedicated break week to consolidate learning and catch up on pending coursework.

Week 6: Practical Data Science (Live)

Apply tree-based models and time series methods to solve classification, regression, and forecasting problems.

Concepts Covered

- Decision Trees: Splits, Entropy, Information Gain, Pruning, and Bias-Variance Tradeoff - Ensemble Learning: Bagging, Bootstrapping, Random Forests, and Feature Sampling - Time Series Analysis: Stationarity, Transformations, and Autocorrelation - AR and ARMA Models: Estimation and Practical Forecasting Applications

Week 7: Deep Learning (Live)

Build and apply neural network architectures, including CNNs and transfer learning pipelines.

Concepts Covered

- Neural Network Foundations: Neurons, Activation Functions, and Multi-Layer Architectures - Training Methods: Cross-Entropy Loss, Gradient Descent, SGD, and Mini-Batch Training - Convolutional Neural Networks: Filters, Convolutions, Pooling, and CNN Architecture - Transfer Learning, Data Augmentation, and Contrastive Learning - Graph Convolutions and Extensions to Non-Image Domains

Week 8: Recommendation Systems (Live)

Build production-ready recommendation systems that handle sparse and time-varying data at scale.

Concepts Covered

- Foundations: Evaluation Metrics, Sparsity, Time-Varying Data, and Modeling Process - Content-Based Recommendation Systems - Collaborative Filtering - Matrix Estimation: Singular Value Thresholding and Time-Aware Matrix Estimation - Neural Approaches to Large-Scale Personalized Recommendations

Week 9: Revision Break

A dedicated break week to consolidate learning and catch up on pending coursework.

Week 10: Elective Project

Develop an end-to-end solution by selecting a problem statement from a chosen domain and applying appropriate data science and AI techniques.

Concepts Covered

- Data Analysis and Visualization - Machine Learning - Practical Data Science - Deep Learning - Recommendation Systems

Week 11: Generative AI and Agentic AI Foundations

Understand the architecture of autonomous AI agents and build functional single-agent systems.

Concepts Covered

- Transition From Reactive LLMs to Autonomous AI Agents - Key Characteristics and Use Cases of AI Agents - Core Agent Components: Memory, Planning, and Tool Use - Designing Task-Oriented Single-Agent Workflows - Applying Agents to Solve Business Problems

Week 12: Building & Evaluating Agentic AI Workflows

Design multi-agent systems and evaluate their performance using production-grade metrics.

Concepts Covered

- Collaborative Multi-Agent System Design and Dynamic Task Routing - Handling Uncertainty and Errors in Agent Workflows - Adaptive RAG in Multi-Agent Generative AI Systems - Evaluation Metrics: Tool Accuracy, ROUGE, BERTScore, and LLM-as-a-Judge

Weeks 13–15: Capstone Project

Design and deliver an end-to-end AI solution for a selected problem statement, integrating concepts and techniques from across the program.

Concepts Covered

- Data Analysis and Visualization / Unsupervised Learning - Machine Learning / Regression - Practical Data Science / Classification - Deep Learning - Recommendation Systems - Generative AI and Agentic AI

Self-Paced Module: Ethical and Responsible AI

Understand and apply ethical principles across the AI lifecycle to design fair, transparent, and responsible AI systems.

Concepts Covered

- Ethical Considerations Across the AI Lifecycle - Identifying and Mitigating Bias in AI Systems - Causality and Its Role in AI-Driven Decision-Making - Privacy Protection and Responsible Data Use - Interdependencies Across AI Applications and Domains - Designing AI Systems Aligned with Societal and Organizational Values

Self-Paced Module | Claude-Based AI Workflows

This module is designed to build practical capability in applying Artificial Intelligence and Data Science using the Claude ecosystem in real-world contexts. Learners build the ability to design, execute, and evaluate AI-driven workflows for real-world applications, supported by ~5 hours of structured learning.

Concepts Covered

- Model selection and Prompt Engineering using Claude Chat - Agentic workflow design and orchestration using Claude CoWork - Plan → Approve → Execute → Iterate framework - Designing workflows with reasoning, tools, and multi-step execution - Applying concepts through real-world case studies

Masterclass on Anthropic

This masterclass covers the Anthropic AI landscape, exploring Claude models, Constitutional AI, and key safety and alignment principles. Learners will apply effective prompting, use the Claude API for tasks and integrations, generate structured outputs, build simple applications, critically compare Claude with other AI models, and evaluate ethical considerations for deploying AI systems.

Sample Case Studies

Apply your learning through real-world case studies guided by global industry experts. Please note: All case studies and projects outlined are indicative and subject to change.

Sales Performance Analysis for a Regional Retailer

RETAIL Learn to analyze store-level KPIs using Python-based data analysis techniques. Use Pandas and NumPy, supported by AI-assisted coding tools, to explore sales trends and generate visual dashboards that support decision-making for category performance. Skills You Will Learn: Python, Data Analysis, Pandas, NumPy, Data Visualization, AI-Assisted Coding

A/B Test Analysis for a Subscription Fitness App

SAAS Learn to evaluate whether a redesigned onboarding flow improves 30-day retention. Apply hypothesis testing and confidence intervals, supported by AI-assisted statistical tools, to draw defensible conclusions from experimental data. Skills You Will Learn: Inferential Statistics, Hypothesis Testing, Confidence Intervals, AI-Assisted Analysis, Data Interpretation

Customer Segmentation for a D2C Beauty Brand

RETAIL Learn to identify distinct customer segments by applying dimensionality reduction and clustering techniques. Use PCA and t-SNE to uncover structure in customer data and apply network analysis to understand referral and influence patterns. Skills You Will Learn: Unsupervised Learning, PCA, t-SNE, Clustering, Network Analysis, Centrality Measures

Credit Default Risk Prediction for a Digital Lender

FINANCE Learn to assess credit default risk by building regression and classification models on loan application data. Use supervised Machine Learning techniques, validated through cross-validation and bootstrapping, to ensure robust and reliable predictions. Skills You Will Learn: Supervised Machine Learning, Regression, Classification, Cross-Validation, Bootstrapping, Model Evaluation

Demand Forecasting and SKU Prioritization for a Quick-Commerce Platform

E-COMMERCE Learn to forecast demand and prioritize inventory by analyzing SKU-level sales patterns. Use decision trees and random forests to classify fast and slow-moving products, and apply time series models such as ARMA to predict daily order volumes and reduce stockouts and inventory waste. Skills You Will Learn: Decision Trees, Random Forests, Time Series Analysis, ARMA Models, Classification, Forecasting

Defect Detection on a Manufacturing Production Line

MANUFACTURING Learn to detect production defects using image-based deep learning models. Train convolutional neural networks on labeled solder joint images to identify defects in real time, applying transfer learning and data augmentation to improve performance with limited data. Skills You Will Learn: Deep Learning, CNNs, Transfer Learning, Data Augmentation, Image Classification, Model Training

Personalized Product Recommendations for an E-commerce Marketplace

E-COMMERCE Learn to design personalized recommendation systems that improve user engagement and conversions. Build a hybrid recommender by combining content-based filtering, collaborative filtering, and matrix estimation techniques to optimize product suggestions and increase average order value. Skills You Will Learn: Recommendation Systems, Content-Based Filtering, Collaborative Filtering, Matrix Estimation, Personalization, Model Evaluation

Employee HR Policy Single-Agent Assistant

HR Learn to build a single-agent AI system that can respond to employee HR policy queries. Design agents with memory, planning, and tool use to deliver accurate, policy-compliant responses and reduce HR support workload. Skills You Will Learn: Agentic AI, Single-Agent Systems, Memory, Planning, Tool Use, Workflow Automation

Banking Customer Service Multi-Agent Copilot

FINANCE Learn to design a multi-agent AI system for customer service in banking. Use dynamic routing and adaptive RAG to generate grounded responses, and apply evaluation metrics to ensure reliability, traceability, and audit readiness. Skills You Will Learn: Multi-Agent AI, Adaptive RAG, Dynamic Routing, Information Retrieval, Evaluation Metrics, AI System Design

Work on hands-on projects and case studies

Engage in practical projects and program-specific case studies using emerging tools and technologies

10+

Case Studies
2 Projects

Industry-Relevant
Access

OpenAI API keys, Codex

Cross-Domain

Elective Project

Description

Build an end-to-end solution by selecting a problem from retail analytics, HR analytics, forecasting, computer vision, or recommendation systems. Work on real-world datasets to apply exploratory data analysis, Machine Learning, deep learning, and recommendation system techniques to solve a chosen business problem.

Skills you will learn

EDA
Machine Learning
Deep Learning
Recommendation Systems
Predictive Modeling

Cross-Domain

Capstone Project

Description

Design and deliver an end-to-end AI solution on a problem statement of your choice, drawing from any topic covered in the program. Work on real-world challenges across data science, Machine Learning, deep learning, recommendation systems, and generative and Agentic AI workflows to build scalable, production-ready solutions.

Skills you will learn

End-to-End AI Systems
Data Science
Machine Learning
Deep Learning
Generative AI
Agentic AI

Master in-demand AI and Data Science tools

Benefit from hands-on experience with 10+ top AI and Data Science tools

Python
Google Colab
VS Code (Visual Studio Code)
OpenAI
n8n
Gemini
Claude
LangChain
LangGraph
Codex

Earn a Professional Certificate in Applied AI & Data Science

Get a certificate of completion from MIT Professional Education and showcase it to your network

* Image for illustration only. Certificate subject to change.

Learn from MIT faculty

Learn from renowned faculty with expertise across AI and data science.

Caroline Uhler

Professor, EECS and IDSS

Expert in computational biology, statistics, and systems.

Award-winning scholar relentlessly driving transformative data insights.

Know More
John N. Tsitsiklis

Clarence J. Lebel Professor, Dept. of Electrical Engineering & Computer Science (EECS) at MIT

Leader in optimization, control, and learning.

Renowned scholar with multiple prestigious accolades.

Know More
Munther Dahleh

William A. Coolidge Professor, EECS and IDSS; Founding Director, IDSS

Trailblazer in robust control and computational design.

Director propelling interdisciplinary research and innovation.

Know More
Stefanie Jegelka

Associate Professor, EECS and IDSS

Expert in algorithms and optimization for AI.

Pioneer advancing theoretical machine learning foundations.

Know More
Devavrat Shah

Andrew (1956) and Erna Viterbi Professor, EECS and IDSS

Renowned expert in large-scale network inference.

Award-winning innovator in data-driven decisions.

Know More

Interact with our mentors

Interact with dedicated and experienced AI and data science experts who will guide you to excellence.

Omar Attia

Senior Machine Learning Engineer Apple (US)
Matt Nickens

Senior Manager, Data Science CarMax
Nirmal Budhathoki

Senior Data Scientist Microsoft
Mohit Khakaria

Senior Machine Learning Engineer Ford Motor Company
Udit Mehrotra

Senior 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

Data Scientist, Bresotec Medical
Saber Fallahpour

Principal Data Scientist, Siemens
Asim Sultan

Senior Machine Learning Engineer RiskHorizon AI
Vibhor Kaushik

Senior Machine Learning Scientist, Amazon
Vaibhav Verdhan

Senior Director Global, AstraZeneca

Watch inspiring success stories

Get authentic feedback from our learners sharing their experiences and insights with the course

Watch story

"MIT faculty are some of the best teachers I have ever had"

MIT faculty explain everything from the very basic theory of every machine learning algorithm to the toughest concepts. Mentors let you see the practical side of things too. This is the course every student who wants to get into data science should take.

Mauricio De Garay

Student ,
Watch story

"From Day 1 was able to solve meaningful, real world, tangible problems"

I had a great experience with world-class instructors and live classes led by industry experts who clearly explained each concept. I highly recommend this program to anyone considering a career shift.

Brooks Christensen

DevOps Engineer , Nielsen
Watch story

"The course was seemless and very engaging"

I enrolled to refresh my technical knowledge, and the projects were highly relevant to real-life scenarios. The mentor was an exceptional coder who explained concepts clearly, and the neural networks sessions were both engaging and fun with great examples.

Gabriela Alessio Robles

Senior Analytics Engineer , Netflix

Get industry-ready with dedicated career support

Get dedicated career support

Access personalized guidance to strengthen your professional brand.
1-on-1 career sessions

Interact with industry professionals to gain actionable career insights.
Resume & LinkedIn profile review

Showcase your strengths with a polished, market-ready profile
Build your project portfolio

Build an industry-ready portfolio to showcase your skills

Course Fees

The course fee is USD 3,900

Advance your career

Engage with MIT faculty and industry experts through live online sessions and recorded lectures
Learn from real-world case studies, projects, and a curriculum infused with the latest advancements in AI
Apply cutting-edge tools and frameworks, including Python, Google Colab, OpenAI, n8n, Gemini, Codex, and more
Benefit from access to OpenAI API keys and Codex for AI-assisted coding provided by Great Learning

Take the next step

00 : 00 : 00

Apply to the program now or schedule a call with a program advisor

Unlock exclusive course sneak peek

Application Closes: 21st May 2026

Talk to our advisor for offers & course details

Registration process

Our registrations close once the requisite number of participants enroll for the upcoming batch. Apply early to secure your seat.

1. Fill application form

Register by completing the online application form.
2. Application screening

A panel from Great Learning will assess your application based on academics, work experience, and motivation.
3. Join program

After a final review, you will receive an offer for a seat in the upcoming cohort of the program.

Eligibility

Exposure to computer programming and a high school-level knowledge of Statistics and Mathematics

Limited seats available

Apply today and become an AI-fluent decision maker

Batch start date

Online · To be announced

Admissions Open

Apply Now

Delivered in Collaboration with:

MIT Professional Education is collaborating with online education provider Great Learning to offer Applied AI and Data Science Program. This program leverages MIT's leadership in innovation, science, engineering, and technical disciplines developed over years of research, teaching, and practice. Great Learning collaborates with institutions to manage enrollments (including all payment services and invoicing), technology, and participant support. Accessibility

Got more questions? Talk to us

Connect with our advisors and get your queries resolved

Speak with our expert +1 617 468 7899 or email to aaidsp.mit@mygreatlearning.com

Frequently asked questions

Frequently Asked Questions

What is the duration of the Applied AI and Data Science Program by MIT Professional Education?

The Applied AI and Data Science Program by MIT Professional Education is a 14-week live virtual program.

Who will teach this Applied AI and Data Science Program?

Live virtual sessions will be delivered by MIT faculty. Experienced program mentors will provide practical guidance and help learners gain a hands-on understanding of core concepts through hands-on projects.

What are the prerequisites for enrolling in the program?

For this Applied AI and Data Science Program, the applicant should have:

Basic exposure to computer programming languages
High school–level knowledge of statistics and mathematics

Can you provide details about the curriculum and course structure of MIT Professional Education's Applied AI and Data Science Program?

The Applied AI and Data Science Program is a 14-week program structured to provide a comprehensive learning experience. The curriculum includes:

Modules on ChatGPT and Generative AI.
Foundation courses to build essential knowledge
Core courses covering key AI and data science concepts
Project submissions to apply learning in practical scenarios
Capstone projects for solving real-world problems
Self-paced modules on ChatGPT and Generative AI to explore emerging technologies

Will I receive a certificate upon completing the program?

Yes. After completing this Applied AI and Data Science Program, you will receive a certificate of completion from MIT Professional Education.

How is the MIT Professional Education Applied AI and Data Science Program different from other data science courses?

The Applied AI and Data Science Program by MIT Professional Education stands out for several reasons:

Offered by MIT Professional Education: Build on seventy years of leadership in engineering, technology, and scientific education.
Learn from MIT faculty: Participate in live virtual sessions by MIT Faculty from the convenience of your home.
Balanced theoretical and practical learning: Understand the value of data through both conceptual and hands-on learning.
AI- and ML-focused projects: Work on case studies and projects that demonstrate how AI can inform data-driven decision-making.
Career support: Benefit from one-on-one career sessions, resume and LinkedIn review, and an e-portfolio showcasing hands-on projects and the capstone.
Mentorship by industry experts: Gain guidance on applying concepts taught in the course to real-world scenarios.
Certificate of Completion: Receive a Certificate of Completion from MIT Professional Education upon successful completion of the program.

What kind of projects or case studies will be included in the program?

The Applied AI and Data Science Program by MIT Professional Education includes hands-on projects within modules as well as a capstone project to reinforce learning. These projects provide practical experience applying AI and machine learning concepts to real-world scenarios. Sample hands-on and capstone projects include:

Sample hands-on projects

Healthcare

Brain Tumor Image Classifier
Building a binary classification model to detect Pituitary Tumors in MRI scans.

Asset Management

Network Stock Portfolio Optimization
Use network analysis and clustering techniques to construct optimized stock portfolios aimed at outperforming market indices like the S&P 500.

Sample Capstone projects

BFSI

Loan Default Prediction
Build a classification model to predict clients who are likely to default on their loans. Give recommendations to the bank on important features to consider while approving a loan.

Marketing

Marketing Campaign Analysis
Apply dimensionality reduction techniques like PCA and t-SNE, along with clustering algorithms such as K-Means and K-Medoids, to uncover meaningful patterns in customer behavior.

Who are the faculty members and instructors who will teach this Applied AI and Data Science Program?

The program features mentorship from Data Science and Machine Learning experts through live and personalized mentored learning sessions.

The program mentors are industry leaders and experts from leading companies like Google, Apple, Amazon, Microsoft, Ford, and more. These program mentors coach you to work on hands-on projects to apply theories to real-world challenges through live and personalized mentored learning sessions. This will help you use and analyze data in the real world and build industry-ready data science skills.

Note: Program faculty is subject to change.

What is the program fee for the Applied AI and Data Science program by MIT Professional Education?

The total program fee is USD 3900.

What is the application process for this course?

The program offers a simplified application process to follow:

Step 1: Register by completing the online application form.

Step 2: A panel from Great Learning will assess your application based on academics, work experience, and motivation.

Step 3: After a final review, you will receive an offer for a seat in the upcoming cohort of the program.

Will I get any assistance during the program?

Yes. You will benefit from a Program Manager who serves as your personal guide throughout the Applied AI and Data Science Program by MIT Professional Education. Your program manager will be your primary point of contact, monitor your progress, and provide support to help you succeed in the course.

What if my question is not covered here?

If you have any other questions, please contact Great Learning through:

Phone: +1 617 468 7899

email: adsp.mit@mygreatlearning.com

Implement AI for Real Business Impact

Applied AI and Data Science Program

Delivered in collaboration with Great Learning

Learn more about the course

Applied AI and Data Science Program

Elevate your career in AI and data science

Earn a certificate of completion from MIT Professional Education

Why choose this program

Live online sessions with MIT faculty

Agentic AI-infused curriculum

Latest AI tech stack

Personalized mentorship by experts

Build an industry portfolio

Personalized mentorship by experts

Agentic AI

Prompt Engineering

Retrieval-Augmented Generation (RAG)

Multi-Agent Systems

LLM Orchestration

Prompt Optimization

AI-Assisted Coding

LLM Evaluation

AI Workflow Design

Generative AI Applications

Data Science

Generative AI

Machine Learning

Data Analysis

Deep Learning

Recommendation Systems

Ethical and Responsible AI

Agentic AI

Prompt Engineering

Retrieval-Augmented Generation (RAG)

Multi-Agent Systems

LLM Orchestration

Prompt Optimization

AI-Assisted Coding

LLM Evaluation

AI Workflow Design

Generative AI Applications

Data Science

Generative AI

Machine Learning

Data Analysis

Deep Learning

Recommendation Systems

Ethical and Responsible AI

This program is ideal for

Comprehensive curriculum

Pre-Work: Python, Data Science, and AI Foundations

Concepts Covered

Week 1: AI-Assisted Python Programming

Concepts Covered

Week 2: AI-Assisted Statistical Analysis and Data Preparation

Concepts Covered

Week 3: Data Analysis and Visualization (Live)

Concepts Covered

Week 4: Machine Learning (Live)

Concepts Covered

Week 5: Revision Break

Week 6: Practical Data Science (Live)

Concepts Covered

Week 7: Deep Learning (Live)

Concepts Covered

Week 8: Recommendation Systems (Live)

Concepts Covered

Week 9: Revision Break

Week 10: Elective Project

Concepts Covered

Week 11: Generative AI and Agentic AI Foundations

Concepts Covered

Week 12: Building & Evaluating Agentic AI Workflows

Concepts Covered

Weeks 13–15: Capstone Project

Concepts Covered

Self-Paced Module: Ethical and Responsible AI

Concepts Covered

Self-Paced Module | Claude-Based AI Workflows

Concepts Covered