Learn AI and Data Science with a Low-Code Approach
Applied AI and Data Science Program
Application closes on
22nd Aug 2025
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Applied AI and Data Science Program
Application closes 22nd Aug 2025
Distinctive features
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Low-code approach
Build AI and data science workflows with minimal coding using intuitive tools. Perfect for professionals looking to advance their proficiency in AI without deep programming experience.
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GenAI-infused curriculum
Covers the latest in Generative AI: Transformers, RAG, Prompt Engineering, and Agentic AI. Designed for real-world business applications.
Unlock real-world impact
Elevate your career in AI and data science
Build your AI and data science proficiency with the latest GenAI tools.
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Apply AI and data science to solve real-world business problems
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Build models for NLP, GenAI, computer vision, and recommendations
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Learn effective data representation for predictive modeling
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Create an industry-ready ePortfolio
Earn a certificate of completion from MIT Professional Education
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#1 in World Universities
QS World University Rankings, 2025
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#1 in AI and Data Science
QS World University Rankings by Subject, 2025
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#2 in National Universities
U.S. News & World Report Rankings, 2024-2025
Key program highlights
Why choose the Applied AI and Data Science Program
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Live online sessions with MIT faculty
Engage in live online sessions with renowned MIT faculty for interactive insights.
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Low-code approach
Build AI and data science skills using low-code tools and techniques, enabling hands-on learning without heavy coding.
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Latest AI tech stack
Explore the latest Generative AI models, including Prompt Engineering and RAG modules.
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Personalized mentorship by experts
Benefit from weekly online mentorship from Data Science and AI industry experts.
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Build an industry portfolio
Work on 50+ case studies, projects, and a capstone project solving real business problems with AI.
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Earn a globally recognized credential
Earn 16 CEUs and a certificate of completion from MIT Professional Education upon completion.
Skills you will learn
PYTHON
DATA ANALYSIS
DATA VISUALIZATION
MACHINE LEARNING
ARTIFICIAL INTELLIGENCE
COMPUTER VISION
DEEP LEARNING
GENERATIVE AI & PROMPT ENGINEERING
RETRIEVAL AUGMENTED GENERATION
AGENTIC AND ETHICAL AI
PYTHON
DATA ANALYSIS
DATA VISUALIZATION
MACHINE LEARNING
ARTIFICIAL INTELLIGENCE
COMPUTER VISION
DEEP LEARNING
GENERATIVE AI & PROMPT ENGINEERING
RETRIEVAL AUGMENTED GENERATION
AGENTIC AND ETHICAL AI
view more
Build your AI and data science proficiency
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86% Execs Report
AI critical to firms
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11.5 Mn+
Jobs in data by 2026
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69% Global Leaders
Say AI #1 for growth
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$103.5 Bn
Analytics market size
- Overview
- Learning Journey
- Curriculum
- Projects
- Tools
- Certificate
- Faculty
- Mentors
- Career support
- Fees
This program is ideal for
Data professionals and managers seeking AI-driven insights
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Extracting Insights from Data
Professionals looking to uncover patterns and derive meaningful, actionable insights from large volumes of data using AI and data science.
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Driving Strategic Impact
Professionals aiming to leverage AI and data science for business strategies, improve decision-making, and lead AI and Generative AI initiatives.
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Building AI Expertise
Those interested in strengthening their understanding of AI, generative AI, and machine learning through hands-on projects and expert-led learning.
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Deepening Technical Skills
Learners with a background in IT, mathematics, or statistics who want to deepen their practical knowledge of advanced AI applications and tools.
Ready to take the next step?
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Explore a sample course from our faculty
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Know more about the case-studies & projects
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Experience a sample mentorship session with an lndustry expert
Application closes in:
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Syllabus designed for professionals
Designed by MIT faculty, the curriculum for the MIT Professional Education Applied AI and Data Science Program (formerly known as the Applied Data Science Program: Leveraging AI for Effective Decision-Making) equips you with the skills, knowledge, and confidence to excel in the industry. It covers key technologies, including artificial intelligence, machine learning, deep learning, recommendation systems, ChatGPT, applied data science with Python, generative AI, and more. The curriculum ensures you are well-prepared to contribute to artificial intelligence and data science initiatives in any organization.
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Low-Code
Approach
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Live Online Sessions
by MIT Faculty
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10+
Emerging Tools and Technologies
Pre-Work: Introduction to Data Science and AI
This module is designed to help you get the most out of the program. We begin an introduction to foundational topics in Python programming, statistics, the Data Science lifecycle, and the evolution of AI and Generative AI. This module is designed to prepare all learners, regardless of prior experience, to confidently engage with the comprehensive curriculum ahead.
- Introduction to the World of Data
- Introduction to Python
- Introduction to Generative AI
- Applications of Data Science and AI
- Data Science Lifecycle
- Mathematics and Statistics behind DS and AI
- History of DS and AI
Weeks 1-2: Foundations – Python and Statistics
In this module, you will build essential programming and statistical skills. Learn to manipulate, visualize, and analyze datasets using:
- NumPy arrays and Functions
- Pandas Series and DataFrames
- Pandas Functions
- Saving and loading datasets using Pandas
- Data Visualization using Seaborn, Matplotlib, and Plotly
- Introduction to Inferential Statistics
- Fundamentals of Probability Distributions
- The Central Limit Theorem
- Hypothesis Testing
- Univariate Analysis
- Bivariate Analysis
- Missing Value Treatment
- Outlier Treatment
Week 3: Data Analysis and Visualization
In this module, you will learn unsupervised learning and dimensionality reduction techniques for pattern discovery.
- Understanding Classification and Clustering Methods
- Supervised Learning
- K-Means Clustering
- Dimensionality Reduction Techniques: PCA and t-SNE
Week 4: Machine Learning
In this module, you will build foundational machine learning models and understand their evaluation.
- Introduction to Supervised Learning
- Linear and Non-Linear Regression
- Causal Inference
- Regression with High-Dimensional Data
- Regularization Techniques
- Model Evaluation
- Cross-Validation
- Bootstrapping
Week 5: Revision Break
Week 6: Practical Data Science
In this module, you will apply real-world techniques in classification, ensemble learning, and forecasting.
- Introduction to Classification
- Logistic Regression
- Decision Trees
- Random Forest
- Type 1 Error & Type 2 Error in Classification Problems
- Hypothesis Testing
Week 7: Deep Learning
In this module, you will explore neural networks and their applications in computer vision and language processing.
- Introduction to Deep Learning
- Neural Network Representations (One Hidden Layer, Hidden Neurons, Multi-class Predictions)
- Introduction to Computer Vision (ANN vs CNN, Basic Terminologies, CNN Architecture)
- Transfer Learning
- Hypothesis Testing
Week 8: Recommendation Systems
In this module, you will design intelligent systems for personalization using a variety of recommendation techniques.
- 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
In this module, you will work independently on a hands-on project that allows you to apply program concepts to a domain of your choice.
Week 10: Generative AI Foundations
In this module, you will understand the architecture, evolution, and foundations of Generative AI.
- 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
In this module, you will learn how Generative AI and Agentic AI can drive business transformation.
- Natural Language Tasks with Generative AI
- Summarization, Classification and Generation
- Retrieval Augmented Generation (RAG)
- Agentic AI
Weeks 12–14: Capstone Project
For your Capstone Project, you will solve a real-world business challenge using techniques from across the program. Projects are guided and evaluated by mentors and reviewed by industry experts.
Module 1 - Introduction to Generative AI
The module covers :
- Overview of ChatGPT and OpenAI
- Timeline of NLP and Generative AI
- Frameworks for understanding ChatGPT and Generative AI
- Implications for work, business and education
- Output modalities and limitations
- Business roles to leverage ChatGPT
- Prompt engineering for fine-tuning outputs
- Practical demonstration and bonus section on RLHF
Module 2 - ChatGPT: The Development Stack
The module covers :
- Mathematical Fundamentals for Generative AI
- VAEs: First Generative Neural Networks
- GANs: Photorealistic Image Generation
- Conditional GANs and Stable Diffusion: Control & Improvement in Image Generation
- Transformer Models: Generative AI for Natural Language
- ChatGPT: Conversational Generative AI
- Hands-on ChatGPT Prototype Creation
- Next Steps for Further Learning and understanding
Work on hands-on projects and case studies
Engage in practical projects and program-specific case studies using emerging tools and technologies across sectors
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50+
Case Studies
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2 Projects
Industry-Relevant
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Capstone Project
Hands-on Learning
Healthcare
Brain Tumor Image Classifier
About the case study
This case study involves building a binary classification model to detect Pituitary Tumors in MRI scans. Learners work with a dataset of 1,000 images (830 for training, 170 for testing), implementing data augmentation to reduce overfitting. Using transfer learning with pre-trained CNN models, learners improve classification accuracy for medical imaging tasks.
Concepts used
- Image Classification
- Data Augmentation
- Transfer Learning
- Pre-trained Models
- Convolutional Neural Networks (CNN)
- Python Programming
Asset Management
Network Stock Portfolio Optimization
About the case study
In this case study, learners use network analysis and clustering techniques to construct optimized stock portfolios aimed at outperforming market indices like the S&P 500. By simulating portfolio performance and evaluating relative returns, this case empowers learners to develop intelligent, data-backed investment strategies.
Concepts used
- Network Analysis
- Portfolio Construction
- Stock Selection
- Clustering Approaches
- Simulation Techniques
- Python Programming
Hospitality
Hotel Booking Cancellation Prediction
About the case study
Learners develop a predictive model to identify likely hotel booking cancellations and no-shows. Using customer and booking data, the model improves resource allocation and revenue management. This case focuses on logistic regression, decision trees, and visualization to derive actionable insights for hospitality management.
Concepts used
- Exploratory Data Analysis
- Data Preprocessing
- Feature Engineering
- Logistic Regression
- Decision Trees
- Random Forests
- Python Programming
- Data Visualization
Entertainment
Movie Recommendation Systems
About the case study
This case study challenges learners to build recommendation engines that enhance user experience on streaming platforms. Using collaborative filtering and matrix factorization, learners develop personalized movie suggestions based on historical interactions, helping boost engagement and satisfaction.
Concepts used
- Exploratory Data Analysis
- Data Preprocessing
- Knowledge/Rank-Based Recommendations
- Similarity-Based Collaborative Filtering
- Matrix Factorization
- Python Programming
Master in-demand AI and Data Science tools
Benefit from hands-on experience with 10+ top AI and Data Science low-code tools
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Python
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NumPy
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Pandas
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Tensorflow
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Transformers
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Seaborn
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Scikit-Learn
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Keras
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
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Devavrat Shah
Professor, EECS and IDSS, MIT
Renowned expert in large-scale network inference.
Award-winning innovator in data-driven decisions.
Know More -
Munther Dahleh
Program Faculty Director, MIT Institute for Data, Systems, and Society (IDSS)
Trailblazer in robust control and computational design.
Director propelling interdisciplinary research and innovation.
Know More -
Caroline Uhler
Henry L. & Grace Doherty Associate Professor, EECS and IDSS, MIT
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 -
Stefanie Jegelka
X-Consortium Career Development Associate Professor, EECS and IDSS, MIT
Expert in algorithms and optimization for AI.
Pioneer advancing theoretical machine learning foundations.
Know More
LinkedIn Profile
University LinkInteract with our mentors
Interact with dedicated and experienced AI and data science experts who will guide you through your learning journey
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Omar Attia
Senior Machine Learning Engineer Apple (US)
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Matt Nickens
Senior Manager, Data Science CarMax
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Fahad Akbar
Senior Manager Data Science Bain & Company
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Udit Mehrotra
Senior Data Scientist Google
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Shannon Schlueter
Director of Data Science Zwift
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Marco De Virgilis
Actuarial Data Scientist Manager Arch Insurance Group Inc.
Watch inspiring success stories
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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 ,
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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
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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
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Get dedicated career support
Access personalized guidance to strengthen your professional brand.
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1-on-1 career sessions
Interact with industry professionals to gain actionable career insights.
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Resume & LinkedIn profile review
Showcase your strengths with a polished, market-ready profile
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Build your project portfolio
Build an industry-ready portfolio to showcase your skills
Course fees
The course fee is 3,900 USD
Advance your career
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Apply AI and data science to solve real-world business problems
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Build models for NLP, GenAI, computer vision, and recommendations
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Learn effective data representation for predictive modeling
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Create an industry-ready ePortfolio
Registration process
Our registrations close once the requisite number of participants enroll for the upcoming batch. Apply early to secure your seat.
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1. Fill application form
Register by completing the online application form.
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2. Application screening
A panel from Great Learning will assess your application based on academics, work experience, and motivation.
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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
Batch start date
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Online · To be announced
Admissions Open
Application Closes
22nd Aug 2025
Batch Profile
The PGP-Data Science class consists of working professionals from excellent organizations and backgrounds maintaining an impressive diversity across work experience, roles and industries.
Batch Industry Diversity
Batch Work Experience Distribution
Batch Education Diversity
The PGP-Data Science learners come from some of the leading organizations.
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