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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
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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.

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
Earn a certificate of completion from MIT IDSS
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

This program is ideal for
Professionals ready to advance their skills in AI, Data Science, and Machine Learning
View Batch Profile
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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.
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Driving Actionable Insights
Individuals seeking to enhance their ability to turn complex data into actionable insights for better business decision-making.
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Leading AI Initiatives
Professionals aiming to lead or contribute to AI and Data Science initiatives across industries.
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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.
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3
hands-on projects
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50+
case studies
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
Earn a certificate of completion from MIT IDSS
Certificate from the MIT Schwarzman College of Computing and IDSS upon successful completion of the program
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World #1
MIT ranks #1 in World Universities – QS World University Rankings, 2025
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U.S. #2
MIT ranks #2 among National Universities – U.S. News & World Report Rankings, 2024–2025

* Image for illustration only. Certificate subject to change.
Program Faculty
Program Mentors
Interact with dedicated and experienced industry experts who will guide you in your learning and career journey
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)
Third Party Credit Facilitators
Check out different payment options with third party credit facility providers




*Subject to third party credit facility provider approval based on applicable regions & eligibility
Application Process
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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
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Online · 9th Aug 2025
Admission closing soon
Batch Profile
The Data Science and Machine Learning class consists of working professionals from excellent organizations and backgrounds maintaining an impressive diversity across work experience, roles and industries.

Industry Diversity

Educational background

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:
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MIT is an abbreviation of the Massachusetts Institute of Technology, one of the world's highest-ranked institutions.
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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.
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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
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Pursue the MIT Data Science certificate course and learn these cutting-edge technologies from 11 award-winning MIT faculty and instructors.
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These award-winning MIT faculty members have designed the curriculum to build industry-valued skills.
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You can demonstrate your Data Science and Machine Learning Leadership by creating a portfolio of 15+ case studies and 3 real-life projects.
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You will work in a robust collaborative environment to communicate with peers in Data Science and Machine Learning.
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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:
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Participants can obtain exclusive discounts on present and future courses offered by MIT IDSS.
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Participants can acquire a subscription to MIT IDSS alumni mailing and newsletter lists.
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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:
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Obtain an understanding of the intricacies of Data Science tools, techniques, and their significance to real-world problems.
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Learn the procedure to implement several Machine Learning techniques for solving complex problems and making data-driven business decisions.
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Explore two noteworthy realms of Machine Learning, Deep Learning & Neural Networks, and learn how to apply these techniques to areas like Computer Vision.
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Choose the process of representing your data while making predictions.
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Obtain an understanding of the theory behind recommendation systems and analyze their applications to numerous industries and business contexts.
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Learn the method to create an industry-ready portfolio of projects for demonstrating your ability to derive business insights from data.
Course Syllabus:
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It commences with the fundamentals of Python programming language (NumPy, Pandas, and Data Visualization) and Statistics for Data Science.
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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.
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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:
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Working professionals, such as early-career professionals or senior managers who want to pursue a career in Data Science and Machine Learning
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Working professionals like Data Scientists, Data Analysts, or ML Engineers interested in leading Data Science and Machine Learning initiatives at their firms or businesses
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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.