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Data Science and Machine Learning: Making Data-Driven Decisions

Data Science and Machine Learning: Making Data-Driven Decisions

Build industry-valued AI, Data Science, and Machine Learning skills

Application closes 10th Jul 2025

Upskill in AI, Data Science & ML

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    Live Mentorship from Industry Practitioners

    Join weekend live virtual sessions with AI, data science and machine learning professionals. Benefit from real-time guidance from experienced practitioners at global organizations.

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    Modules on Responsible AI and Generative AI

    Deepen understanding of ethical AI with the Responsible AI module and explore innovations in Generative AI, covering tools, techniques, and real-world applications.

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Program Outcomes

Key takeaways for career success in AI, Data Science, and Machine Learning

Designed for learners to gain hands-on experience and build industry-valued skills

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    Understand the intricacies of Data Science and Artificial Intelligence techniques and their applications to real-world problems

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    Implement various Machine Learning techniques to solve complex problems and make data-driven business decisions

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    Explore two major realms of Artificial Intelligence: Machine Learning and Deep Learning, and understand how they apply to domains such as Computer Vision and Recommendation Systems

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    Choose how to represent your data effectively when making predictions

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    Explore the practical applications of Recommendation Systems across various industries and business contexts

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    Build an industry-ready portfolio of projects and demonstrate your ability to extract valuable business insights from data

Earn a certificate of completion from MIT IDSS

  • U.S. News & World Report, 2024

    U.S. #2

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

  • QS World University Rankings, 2025

    World #1

    QS World University Rankings, 2025

Key program highlights

Why choose the Data Science and Machine Learning program

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    Learn from MIT faculty

    Learn from the vast knowledge of MIT AI, Data Science and Machine Learning faculty through recorded sessions.

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    Collaborative peer networking

    Engage in a collaborative environment, networking with global AI, Data Science, and Machine Learning peers.

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    Build your AI, Data Science, and Machine Learning Portfolio

    Showcase your AI and data science skills with 3 real-world projects and 50+ hands-on case studies in your e-portfolio.

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    Personalized mentorship sessions

    Benefit from personalized weekend mentorship by experienced AI, Data Science and ML practitioners from leading global organizations.

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    Dedicated Program support

    Connect with dedicated program managers to assist with queries and guide you throughout the course.

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    Generative AI Masterclasses

    Get access to 3 masterclasses on Generative AI and its use cases by industry experts.

Skills you will learn

Python

Machine Learning

Deep Learning

Recommendation Systems

Computer Vision

Predictive Analytics

Generative AI

Prompt Engineering

Retrieval-Augmented Generation

Ethical AI

Python

Machine Learning

Deep Learning

Recommendation Systems

Computer Vision

Predictive Analytics

Generative AI

Prompt Engineering

Retrieval-Augmented Generation

Ethical AI

view more

  • Overview
  • Curriculum
  • Projects
  • Tools
  • Certificate
  • Faculty
  • Mentors
  • Reviews
  • Fees
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This program is ideal for

Professionals ready to advance their skills in AI, Data Science, and Machine Learning

View Batch Profile

  • Building Expertise for AI-driven Roles

    Professionals looking to build expertise in AI, Data Science, and Machine Learning through hands-on projects and real-world applications.

  • Driving Actionable Insights

    Individuals seeking to enhance their ability to turn complex data into actionable insights for better business decision-making.

  • Leading AI Initiatives

    Professionals aiming to lead or contribute to AI and Data Science initiatives across industries.

  • Solving Business Challenges

    Professionals interested in applying advanced AI techniques like Generative AI, Deep Learning, and Recommendation Systems to solve business challenges.

Curriculum

Developed by MIT IDSS faculty, this curriculum immerses you in today’s most cutting-edge data science and AI technologies - from machine learning and deep learning to recommendation systems, network analytics, time-series forecasting, and the transformative capabilities of ChatGPT and Generative AI.

Pre-work

Foundations of Data Science and AI 


  • 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 Data Science and AI 
  • History of Data Science and AI

Week 0: Data Science and AI Applications

This module provides an in-depth exploration of the entire lifecycle of an AI application through detailed case study analysis. By examining real-world scenarios, you will gain a comprehensive understanding of how AI tackles complex business challenges, equipping you with insights into AI's role in driving business solutions from conception to execution.

Weeks 1 and 2: Foundations of AI

This course focuses on essential data science skills, laying the foundation for understanding artificial intelligence. You will learn to efficiently manage and analyze data using Python's NumPy and Pandas libraries, create data visualizations, and apply statistical techniques like inferential statistics and hypothesis testing to draw meaningful insights from data.

Python for Data Science

  • NumPy 
  • Pandas 
  • Data Visualization

Stats for Data Science

  • Descriptive Statistics 
  • Inferential Statistics

Week 3: Masterclass on Data Analysis with Generative AI

Explore how Generative AI can analyze and synthesize unstructured data, empowering smarter, faster decision-making in a masterclass.

Week 4: Making Sense of Unstructured Data

This course focuses on the techniques to extract insights from unstructured data. You will learn clustering methods to group similar data points and apply dimensionality reduction techniques such as PCA and t-SNE, simplifying complex data while preserving essential patterns and relationships.

  • Supervised & Unsupervised Learning 
  • K-Means Clustering 
  • Dimensionality Reduction techniques - PCA, t-SNE

Week 5: Project Week and GenAI Masterclass

  • Project on Clustering and PCA 
  • Masterclass on Learning from Text Data

Week 6: Regression and Prediction

This course explores the fundamentals of regression and prediction, guiding you through linear and non-linear regression methods to model relationships in data. You will delve into causal inference to understand cause-and-effect relationships, tackle high-dimensional data challenges with regularization techniques, and refine your skills in model evaluation through cross-validation and bootstrapping, ensuring accurate and reliable predictive models.

  • Linear and Non-Linear Regression 
  • Causal Inference 
  • Regression with High-Dimensional Data 
  • Regularization Techniques 
  • Model Evaluation 
  • Cross-validation and Bootstrapping

Week 7: Classification and Hypothesis Testing

This course offers an overview of classification and hypothesis testing techniques. You will learn classification methods such as logistic regression, decision trees, and random forests, along with understanding Type 1 and Type 2 errors in classification. The course also includes essential hypothesis testing skills to validate models and interpret data results effectively.

  • Introduction to Classification 
  • Logistic Regression 
  • Decision Trees and Random Forest 
  • Type 1 Error & Type 2 Error 
  •  Hypothesis Testing

Week 8: Project Week and GenAI Masterclass

Consolidate your learning through a hands-on classification project and explore text labeling with Generative AI techniques.

  • Project on Machine Learning Classification 
  • Masterclass on AI-Powered Text Labeling

Week 9: Deep Learning and Computer Vision

This course covers the essentials of deep learning and computer vision. You will learn about neural networks, from basic single-layer models to complex multi-layer architectures for multi-class predictions. Explore computer vision, focusing on CNNs, their architecture, and key concepts, while also understanding transfer learning for efficient image processing.

  • Introduction to Deep Learning 
  • Neural Network Representations 
  • Introduction to Computer Vision 
  • CNN architecture 
  • Transfer Learning

Week 10: Recommendation Systems

This course delves into the core concepts of recommendation systems, exploring techniques like clustering, collaborative filtering, and singular value thresholding to create personalized user experiences effectively.

  • Recommendation Systems 
  • Types of Recommendation Systems 
  • Clustering 
  • Collaborative Filtering 
  • Single Value Thresholding

Week 11: Ethical and Responsible AI

In this course, you will explore the AI lifecycle, identify bias with real-world examples, and understand causality and privacy concerns. The course also covers the interconnections and domains of AI systems, focusing on their interdependency and feedback mechanisms to ensure responsible AI development and deployment.

  • Introduction to AI Lifecycle 
  • Introduction to Bias and its Examples 
  • Introduction to Causality and Privacy 
  • Interconnections and Domains 
  • Interdependency and Feedback in AI Systems

Week 12: Project Week

Combine all your skills in a final project, building an end-to-end data-driven solution.

  • Project on Recommendation System

Self-Paced Modules

Extend your skills in specialized areas through optional, advanced modules.

Networking and Graphical Models

Explore methods for analyzing and modeling complex networks using graphical models to understand interactions and correlations.

Predictive Analytics

Master techniques for building accurate predictive models from temporal data, including feature engineering and model evaluation.

Prompt Engineering

Learn to design effective prompts and techniques for interacting with large language models.

Generative AI Development Stack

Learn how to build Generative AI solutions using the latest tools, models, and components in the modern AI development stack.

Projects and Case Studies

The program follows a learn-by-doing pedagogy, helping you build your skills through real-world case studies and hands-on practice. Below are samples of potential project topics and case studies you will work on.

  • 3

    hands-on projects

  • 50+

    case studies

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Retail

Customer Personality Segmentation

About the Project

It focuses on customer segmentation, a common practice in retail to improve marketing strategies, customer retention, and resource allocation. By analyzing customer demographics, purchasing behavior, and interactions with marketing campaigns, the retail company aims to understand its customer base better and tailor its offerings to meet the preferences and needs of different customer segments.

Skills you will learn

  • Python
  • Exploratory Data Analysis
  • Data Pre-processing
  • K-means Clustering
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EdTech (Educational Technology)

Potential Customers Prediction

About the Project

The problem statement involves predicting potential customers in this rapidly growing sector by analyzing leads and their interactions with the company, ExtraaLearn.

Skills you will learn

  • Python
  • Decision tree
  • Random forest
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E-Commerce and Technology

Amazon Product Recommendation System

About the Project

This project involves developing a product recommendation system for Amazon, focusing on providing personalized suggestions based on users' previous product ratings. By utilizing techniques like collaborative filtering, the goal is to enhance user engagement and satisfaction, ultimately driving sales and improving the user experience on the platform.

Skills you will learn

  • Python
  • Knowledge/Rank-based
  • Similarity-Based Collaborative filtering
  • Matrix Factorization Based Collaborative Filtering
  • Clustering-based recommendation system
  • Content-based collaborative filtering
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Healthcare

Hospital Loss Prediction

About the Project

This case study focuses on building a regression-based machine learning solution to predict the Length of Stay (LOS) of patients using data available at admission and from initial tests. The goal is to identify key factors influencing LOS, derive actionable insights, and support hospital policy planning to enhance infrastructure and revenue generation.

Skills you will learn

  • Exploratory Data Analysis
  • Data Preprocessing
  • Regression Modeling
  • Data Interpretation
  • Python Programming
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Human Resources

HR Employee Attrition Prediction

About the Project

This case study involves developing a predictive model to identify employees at risk of attrition using organizational data. By uncovering patterns in employee behavior and characteristics, the model helps to optimize retention efforts and reduce costs by targeting incentives only to high-risk individuals.

Skills you will learn

  • Exploratory Data Analysis
  • Data Preprocessing
  • Logistic Regression
  • Linear Discriminant Analysis (LDA)
  • Quadratic Discriminant Analysis (QDA)
  • Python Programming
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Geospatial Technology

Street View Housing Number Digit Recognition

About the Project

This case study focuses on building a deep learning solution to recognize house numbers from street-level images using the SVHN dataset. The model automates the transcription of numeric address data from image patches, supporting geospatial applications such as improving digital map accuracy and pinpointing building locations.

Skills you will learn

  • Exploratory Data Analysis
  • Data Preprocessing
  • Artificial Neural Networks (ANNs)
  • Convolutional Neural Networks (CNNs)
  • Python Programming
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E-commerce

Book Recommendation System

About the Project

This case study explores the development of a book recommendation system that suggests titles based on user preferences. By leveraging various collaborative filtering techniques and user-item interaction data, the system delivers relevant suggestions to enhance user experience and drive sales. Widely applicable across major e-commerce platforms, such systems help reduce browsing time and increase purchase value.

Skills you will learn

  • Exploratory Data Analysis
  • Data Preprocessing
  • Knowledge/Rank-Based Recommendations
  • Similarity-Based Collaborative Filtering
  • Matrix Factorization
  • Python Programming

Languages and Tools covered

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    Python

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    NumPy

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    Keras

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    Tensorflow

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    Matplotlib

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    Skitlearn

  • And More...

Earn a certificate of completion from MIT IDSS

Certificate from the MIT Schwarzman College of Computing and IDSS upon successful completion of the program

  • World #1

    World #1

    MIT ranks #1 in World Universities – QS World University Rankings, 2025

  • U.S. #2

    U.S. #2

    MIT ranks #2 among National Universities – U.S. News & World Report Rankings, 2024–2025

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* Image for illustration only. Certificate subject to change.

Program Faculty

  • Caroline Uhler - Faculty Director

    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.

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  • Munther Dahleh - Faculty Director

    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
  • Devavrat Shah - Faculty Director

    Devavrat Shah

    Professor, EECS and IDSS, MIT

    Renowned expert in large-scale network inference.

    Award-winning innovator in data-driven decisions.

    Know More
  • Stefanie Jegelka - Faculty Director

    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
  • John N. Tsitsiklis - Faculty Director

    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
  • Tamara Broderick - Faculty Director

    Tamara Broderick

    Associate Professor, EECS and IDSS, MIT.

    Know More
  • Philippe Rigollet - Faculty Director

    Philippe Rigollet

    Professor, Mathematics and IDSS, MIT

    Know More
  • Victor Chernozhukov - Faculty Director

    Victor Chernozhukov

    Professor, Economics and IDSS, MIT

    Know More
  • Guy Bresler - Faculty Director

    Guy Bresler

    Associate Professor, EECS and IDSS, MIT

    Know More
  • David Gamarnik - Faculty Director

    David Gamarnik

    Nanyang Technological University Professor of Operations Research, Sloan School of Management and IDSS, MIT

    Know More
  • Kalyan Veeramachaneni - Faculty Director

    Kalyan Veeramachaneni

    Principal Research Scientist at the Laboratory for Information and Decision Systems, MIT.

    Know More
  • Jonathan Kelner - Faculty Director

    Jonathan Kelner

    Professor, Mathematics, MIT

    Know More
  • Ankur Moitra - Faculty Director

    Ankur Moitra

    Rockwell International Career Development Associate Professor, Mathematics and IDSS, MIT

    Know More

Program Mentors

Interact with dedicated and experienced industry experts who will guide you in your learning and career journey

  •  Bradford Tuckfield - Mentor

    Bradford Tuckfield

    Founder and Data Science Consultant Kmbara
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  •  Vaibhav Verdhan - Mentor

    Vaibhav Verdhan

    Analytics Leader, Global Advanced Analytics AstraZeneca
    AstraZeneca Logo
  •  Mayan Murray - Mentor

    Mayan Murray

    Senior Data Scientist and UX Consultant IBM
    IBM Logo
  •  Vibhor Kaushik - Mentor

    Vibhor Kaushik

    Data Scientist Amazon
    Amazon Logo
  •  Amit Agarwal - Mentor

    Amit Agarwal

    Senior Data Scientist Oracle
    Oracle Logo
  •  Kemal Yilmaz - Mentor

    Kemal Yilmaz

    Senior Data Scientist Walmart Connect
    Walmart Connect Logo
  •  Xiaojun Su - Mentor

    Xiaojun Su

    Data Science Product Manager Unilever
    Unilever Logo
  •  Juan Castillo - Mentor

    Juan Castillo

    Machine Learning Engineer SEPHORA
    SEPHORA Logo
  •  Andrew Marlatt - Mentor

    Andrew Marlatt

    Data Scientist - Revenue Expansion Shopify
    Shopify Logo
  •  Rohit Dixit - Mentor

    Rohit Dixit

    Senior Data Scientist Siemens Healthineers
    Siemens Healthineers Logo
  •  Srikanth Pyaraka - Mentor

    Srikanth Pyaraka

    Data Science Product Manager Verizon
    Verizon Logo
  •  Angel Das - Mentor

    Angel Das linkin icon

    Data Science Consultant IQVIA Asia Pacific
    IQVIA Asia Pacific Logo
  •  Shirish Gupta - Mentor

    Shirish Gupta

    Lead Data Scientist Novartis
    Novartis Logo
  •  Vanessa Afolabi - Mentor

    Vanessa Afolabi

    Senior Data Scientist Loblaw Companies Limited
    Loblaw Companies Limited Logo
  •  Thinesh Pathmanathan - Mentor

    Thinesh Pathmanathan

    Data Scientist TD
    TD Logo
  •  Grivine Ochieng - Mentor

    Grivine Ochieng

    Lead Data Engineer Xetova
    Xetova Logo

Watch inspiring success stories

  • learner image
    Watch story

    "The people behind the program were amazing, I believe this was best part of the program"

    The favourite part was the hackathon competition, where we had to combine everything that we had learnt and build the model

    Arlindo Almada

    ,

  • learner image
    Watch story

    "The program helped me restructure my professional life after COVID"

    Francisco JosÉ Valencia Alaix

    ,

  • learner image
    Watch story

    ""

    Towo Adeyemi

    ,

Course fees

The course fee is 2,500 USD

Invest in your career

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    Learn from world-renowned MIT IDSS faculty and top industry leaders

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    Build an impressive portfolio with 3 projects and 50+ case studies

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    Get personalized assistance with a dedicated Program Manager from Great Learning

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    Earn a certificate of completion from MIT IDSS and 8.0 Continuing Education Units (CEUs)

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Easy payment plans

Avail our EMI options & get financial assistance

Third Party Credit Facilitators

Check out different payment options with third party credit facility providers

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*Subject to third party credit facility provider approval based on applicable regions & eligibility

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Unlock exclusive course sneak peek

Application Closes: 10th Jul 2025

Application Closes: 10th Jul 2025

Talk to our advisor for offers & course details

Application Process

  • steps icon

    1. Fill application form

    Apply by filling a simple online application form.

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    2. Application Screening

    A panel from Great Learning will review your application to determing your fit for the program.

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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.

Batch start date

  • Online · 12th Jul 2025

    Admission closing soon

Got more questions? Talk to us

Connect with a program advisor and get your queries resolved

Speak with our expert +1 617 539 7216 or email to dsml.mit@mygreatlearning.com

career guidance

Delivered in Collaboration with:

MIT Professional Education is collaborating with online education provider Great Learning to offer Data Science and Machine Learning: Making Data-Driven Decisions. 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