phone iconSpeak with our expert +1 617 539 7216

Learn more about the course

Get details on syllabus, projects, tools, and more

Name
Email
Mobile Number

By submitting this form, you consent to our Terms of Use & Privacy Policy and to be contacted by us via Email/Call/Whatsapp/SMS.

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 31st Jul 2025

Upskill in AI, Data Science & ML

  • List icon

    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.

  • List icon

    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.

overview icon

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

  • List icon

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

  • List icon

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

  • List icon

    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

  • List icon

    Choose how to represent your data effectively when making predictions

  • List icon

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

  • List icon

    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

  • List icon

    Learn from MIT faculty

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

  • List icon

    Collaborative peer networking

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

  • List icon

    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.

  • List icon

    Personalized mentorship sessions

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

  • List icon

    Dedicated Program support

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

  • List icon

    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
optimal icon

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.

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.

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

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

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.

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

project icon

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
project icon

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
project icon

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
project icon

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
project icon

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
project icon

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
project icon

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

  • tools-icon

    Python

  • tools-icon

    NumPy

  • tools-icon

    Keras

  • tools-icon

    Tensorflow

  • tools-icon

    Matplotlib

  • tools-icon

    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

certificate image

* 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.

    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
  • 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
  • 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
  • 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

Program Mentors

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

  •  Omar Attia - Mentor

    Omar Attia

    Senior Machine Learning Engineer Apple (US)
    Apple (US) Logo
  •  Bhaskarjit Sarmah  - Mentor

    Bhaskarjit Sarmah linkin icon

    Head RQA AI Labs, BlackRock
    Company Logo
  •  Vibhor Kaushik - Mentor

    Vibhor Kaushik

    Data Scientist Amazon
    Amazon Logo
  •  Matt Nickens - Mentor

    Matt Nickens

    Senior Manager, Data Science CarMax
    CarMax Logo
  •  Nirmal Budhathoki  - Mentor

    Nirmal Budhathoki

    Senior Data Scientist Microsoft
    Microsoft Logo
  •  Mohit Khakaria  - Mentor

    Mohit Khakaria

    Senior Machine Learning Engineer Ford Motor Company
    Ford Motor Company Logo
  •  Udit Mehrotra - Mentor

    Udit Mehrotra

    Senior Data Scientist Google
    Google Logo
  •  Andrew Marlatt - Mentor

    Andrew Marlatt

    Data Scientist - Revenue Expansion Shopify
    Shopify Logo
  •  Vaibhav Verdhan - Mentor

    Vaibhav Verdhan

    Analytics Leader, Analítica Global
    Analítica Global Logo
  •  Amish Suchak  - Mentor

    Amish Suchak

    Data Science Team Lead XSOLIS
    XSOLIS Logo
  •  Nirupam Sharma  - Mentor

    Nirupam Sharma

    Data Science Vice President Big Village
    Big Village Logo
  •  Deepa Krishnamurthy  - Mentor

    Deepa Krishnamurthy

    Director, AI Solutions Engineering Koru
    Koru Logo
  •  Marco De Virgilis - Mentor

    Marco De Virgilis

    Actuarial Data Scientist Manager Arch Insurance Group Inc.
    Arch Insurance Group Inc. Logo
  •  Cristiano Santos De Aguiar  - Mentor

    Cristiano Santos De Aguiar

    Biomedical Machine Learning Engineer Oncoustics
    Oncoustics Logo
  •  Saber Fallahpour  - Mentor

    Saber Fallahpour

    Principal Data Scientist Altair
    Altair Logo
  •  Asim Sultan  - Mentor

    Asim Sultan

    Senior Machine Learning Engineer RiskHorizon AI
    RiskHorizon AI 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

    " Mentors help you understand difficult concepts and complete the course"

    Studying this course has placed me in a better position to offer good counseling in my field. I am going to stretch myself to work as a Data Scientist in the business industry. I see this opportunity as a dream come true.

    Berthy Buah

    STMIE Coordinator , Ghana Education Service

  • learner image
    Watch story

    "Building Confidence in Big Data Management Without Prior Experience"

    Joined the program to learn handling big data and exceeded expectations. Gained valuable skills in Python and Machine Learning. Highly recommend it for anyone starting their data analytics journey!

    Chun Wing Ip

    Student , University Of Sydney

Course fees

The course fee is 2,500 USD

Invest in your career

  • benifits-icon

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

  • benifits-icon

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

  • benifits-icon

    Get personalized assistance with a dedicated Program Manager from Great Learning

  • benifits-icon

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

project icon

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

benifits-icon benifits-icon benifits-icon benifits-icon

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

timer
00 : 00 : 00

Unlock exclusive course sneak peek

Application Closes: 31st Jul 2025

Application Closes: 31st 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.

  • steps icon

    2. Application Screening

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

  • steps icon

    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 · 9th Aug 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

Introduction to the Data Science & Machine Learning Course from MIT for Working Professionals

Numerous professional courses are available across the globe for Data Science and Machine Learning. Yet, there are several reasons for working professionals to register in this Machine Learning and Data Science professional certificate program from MIT IDSS, collaborating with Great Learning. The reasons are drafted below:

  • MIT is an abbreviation of the Massachusetts Institute of Technology, one of the world's highest-ranked institutions.

  • According to rankings by QS World University Rankings 2023, MIT has ranked #1 university globally, and according to rankings by the U.S. News and World Report 2023, MIT is ranked #2 in the world.

  • The objective of MIT IDSS is to extend education and research in state-of-the-art analytical techniques in statistics and data science, information and decision systems, and the social sciences, and to apply these techniques to address complex societal challenges in a miscellaneous set of areas like finance, urbanization, social networks, energy systems, and health.


Benefits of Pursuing MIT Data Science Certificate Course

  • Pursue the MIT Data Science certificate course and learn these cutting-edge technologies from 11 award-winning MIT faculty and instructors.

  • These award-winning MIT faculty members have designed the curriculum to build industry-valued skills.

  • You can demonstrate your Data Science and Machine Learning Leadership by creating a portfolio of 15+ case studies and 3 real-life projects.

  • You will work in a robust collaborative environment to communicate with peers in Data Science and Machine Learning.

  • Obtain live mentorship sessions and guidance from Machine Learning and Data Science professionals on applying concepts taught by the faculties.


Alumni IDSS Benefits

Have a glance at the benefits offered by IDSS alumni:

  • Participants can obtain exclusive discounts on present and future courses offered by MIT IDSS.

  • Participants can acquire a subscription to MIT IDSS alumni mailing and newsletter lists.

  • Participants can acquire membership to advance notice of upcoming events and courses.


Details about MIT Data Science Course

In this comprehensive MIT Data Science online course, the participants will grasp all the critical skills required to master Data Science and Machine Learning. Let’s go through the extensive details about the course in Data Science for working professionals:

Course Learnings:

  • Obtain an understanding of the intricacies of Data Science tools, techniques, and their significance to real-world problems.

  • Learn the procedure to implement several Machine Learning techniques for solving complex problems and making data-driven business decisions.

  • Explore two noteworthy realms of Machine Learning, Deep Learning & Neural Networks, and learn how to apply these techniques to areas like Computer Vision.

  • Choose the process of representing your data while making predictions.

  • Obtain an understanding of the theory behind recommendation systems and analyze their applications to numerous industries and business contexts.

  • Learn the method to create an industry-ready portfolio of projects for demonstrating your ability to derive business insights from data.

Course Syllabus:

  • It commences with the fundamentals of Python programming language (NumPy, Pandas, and Data Visualization) and Statistics for Data Science.

  • Afterward, participants will learn Machine Learning techniques, including Supervised and Unsupervised Learning Techniques, Clustering, Regression, Decision Trees, Random Forests, Classification and Hypothesis Testing, and several other algorithms.

  • Moving forward, participants will learn Deep Learning, Recommendation Systems, Networking & Graphical Models, Predictive Analysis, and Feature Engineering.

[Explore MIT Data Science Course Syllabus]


Course Eligibility:

  • Working professionals, such as early-career professionals or senior managers who want to pursue a career in Data Science and Machine Learning

  • Working professionals like Data Scientists, Data Analysts, or ML Engineers interested in leading Data Science and Machine Learning initiatives at their firms or businesses

  • Entrepreneurs interested in innovation with the assistance of Data Science and Machine Learning techniques


MIT Data Science for Working Professionals Course Duration

This professional course is for 12 weeks with recorded lectures from award-winning, world-renowned MIT faculty members and live mentorship sessions from industry experts.

Secure a Data Science Professional Certificate, along with Machine Learning from MIT IDSS

After successfully pursuing this course, you will secure a professional certificate in Data Science and Machine Learning: Making Data-Driven Decisions from MIT IDSS.