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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 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.

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.
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.
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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
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*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 · 12th Jul 2025
Admission closing soon
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
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
