MIT Professional Education's Data Science Course

Applied Data Science Program: Leveraging AI for Effective Decision-Making

Become a data-driven decision maker. Learn applied Data Science, AI, and Machine Learning with live virtual sessions from MIT Faculty and immersive hands-on projects.

12 Weeks
Certificate of Completion by MIT Professional Education
Interactive Online Mentorship

MIT Professional Education's Applied Data Science Program: Leveraging AI for Effective Decision-Making, with curriculum developed and taught by MIT faculty, is delivered in collaboration with Great Learning.

Why Join the Applied Data Science Program: Leveraging AI for Effective Decision-Making

Learn from Top Faculty

Live virtual sessions from world-renowned MIT Faculty
Curriculum designed to build industry-valued skills: Deep Learning, Machine Learning with Python.

Mentorship and Program Support

Live mentorship and guidance from Data Science practitioners on weekends
Personalized learning assistance from a dedicated Program Manager

Practical, Hands-on Training

Complete hands-on Data Science training through 6 projects under the guidance of industry experts
Take on a business challenge and showcase your Data Science skills with the 3-week Capstone Project.

Live Virtual Sessions from world-renowned MIT Faculty

Personalised mentorship and guidance from data science practitioners

Hands-on training via 2 projects and 1 Capstone

Curriculum covering Deep Learning, Machine Learning, with Python

Certificate of Completion from MIT Professional Education

MIT Applied Data Science Program Certificate

#1

MIT's Rank in World Universities

QS World University Rankings, 2025

#2

MIT's Rank in National Universities

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

Note: The image is for illustrative purposes only. The actual certificate may be subject to change at the discretion of MIT Professional Education.

Curriculum Designed for Professionals

Curriculum designed by MIT faculty in Data Science and Machine Learning
Become a Data Science decision maker by learning Deep Learning, Machine Learning, Recommendation Systems, and more.
Taught in Python

MIT Professional Education's Applied Data Science Program: Leveraging AI for Effective Decision-Making curriculum is designed by MIT faculty to equip you with the necessary skills, knowledge, and confidence to excel in the industry. It covers the technologies, including Machine Learning, Deep Learning, Recommendation Systems, ChatGPT, Applied Data Science with Python, Generative AI, and others. The curriculum ensures that you are well-prepared to contribute to Data Science efforts in any organization.

Required Weekly Commitment: For 5 weeks of MIT Faculty live lectures, each week amounts to an average time commitment of 15-18 hours. For the remaining 7 weeks, an average time commitment of 12-14 hours per week is expected from the learners. (This includes live virtual sessions, mentor learning sessions, self-study, etc.).

Download Curriculum

Pre-work: Foundations of Applied Data Science

To help you get the most out of the program, we provide access to prework content that covers foundational topics in Python programming, statistics, and an introduction to data science and AI.

While prework is optional and does not count toward program completion, it is strongly recommended—especially for learners who do not have prior experience in programming or statistics. Going through the material early will better prepare you for the core courses with MIT faculty.

Estimated Time Commitment:

Mandatory content: ~2.5 hours
Optional content: ~9 hours.
We also conduct optional live sessions over the weekend before the program launch to support your learning and answer any questions.

Weeks 1-2

Get ready to lay the groundwork for success! Our MIT Professional Education Data Science and Machine Learning Program starts with an intensive two-week module covering essential Data Science concepts. This foundational training sets the stage for your continued growth and achievement throughout the course.

Module 1: Foundations - Python and Statistics

1 Project

The first module in the program for applied Data Science begins with the foundations, which covers Python and Statistics foundations.

Python Foundations - Libraries: Pandas, NumPy, Arrays and Matrix handling, Visualization, Exploratory Data Analysis (EDA)
Pandas is a commonly used library in Python, which is used to analyse and manipulate data.
NumPy is a package in the Python library, where you can use this package for scientific computing to work with arrays.
An array is a data structure that stores various elements or items at contiguous memory locations.
A matrix is a two dimensional (2D) array where data (elements/items) is stored in the format of rows and columns.
Visualization is the process to represent data and information in a graphical form.
Exploratory Data Analysis (EDA) enables you to uncover patterns and insights frequently with visual methods within some data.
Statistics Foundations: Basic/Descriptive Statistics, Distributions (Binomial, Poisson, etc.), Bayes, Inferential Statistics
Descriptive Statistics is a method that helps you study data analysis using multiple data sets by describing and summarizing them. For example, the data set can either be a collection of the population in a neighbourhood or the marks a sample of 100 students achieved.
A Distribution is a statistical function used to report all the probable values that a random variable takes within a certain range.
Bayes Theorem is a mathematical formula that is named after Thomas Bayes. This theorem helps you determine conditional probability.
Inferential Statistics is a method that lets you explore basic concepts on using data for estimation and assess theories with the help of Python.

Week 3

In the third week, you will learn about bootstrapping data to make it ML/AI ready, along with the practical applications of the techniques used.

Module 2: Data Analysis and Visualization

1 Project

The next module in this applied Data Science course will teach you all the essentials about data analysis and visualization.

Exploratory Data Analysis, Visualization (PCA and t-SNE) for visualization and batch correction
This chapter will lecture you on all the essential topics about EDA and visualization.
Introduction to Unsupervised Learning: Clustering includes- Hierarchical, K-Means, DBSCAN, Gaussian Mixture
Unsupervised learning is a technique that helps you analyze and cluster unlabelled data sets. Clustering is a technique that clusters or groups data.
In this chapter, you will learn more about unsupervised learning and clustering techniques, like Hierarchical, K-Means, DBSCAN, and Gaussian Mixture.
Networks: Examples (data as network versus network to represent dependence among variables), determine important nodes and edges in a network, clustering in a network
In this chapter, you will learn about networks and various examples of a network, like data as a network versus network to represent dependence among variables, determine important nodes and edges in a network, and clustering in a network.

Week 4

In this week, you will explore the fundamentals of Supervised Machine Learning and Prediction, including some key algorithms and widely-used techniques.

Module 3: Machine Learning

1 Project

The next module in this MIT Professional Education Applied Data Science Program will teach you about Machine Learning, which covers supervised learning and model evaluation. Machine Learning is an application of Artificial Intelligence, which studies computer algorithms and improves automatically through experience and data usage.

Introduction to Supervised Learning - Regression
Supervised learning is a technique that helps you analyze and cluster labelled data sets. Regression is a statistical technique in machine learning that manages the relationship between dependent and independent variables with the help of one or more independent variables.
Introduction to Supervised Learning - Classification
Classification, as the name implies, is a procedure to classify/categorize a data set into various categories. This can be performed on both structured and unstructured data.
Model Evaluation - Cross Validation and Bootstrapping
Model Evaluation is a technique used for machine learning models, which estimates the accuracy of these models on future data. This chapter will prepare you for evaluating machine learning models using model evaluation techniques, like Cross Validation and Bootstrapping.

Week 5 - Learning Break

Week 6

In the sixth week of the program, you will explore key areas of Data Science that are highly applicable to business and decision-making contexts along with their practical applications.

Module 4: Practical Data Science

1 Project

The next module in the program for applied Data Science teaches you about decision trees, random forests, and time series analysis.

Decision Trees
A Decision Tree is a popular supervised machine learning algorithm, which is used for both classification and regression problems. It is a hierarchical structure in which the internal nodes denote the dataset features, branches indicate the decision rules, and each leaf node represents the result.
Random Forest
Random Forest is another popular supervised machine learning algorithm. As the name implies, it consists of multiple decision trees on the various subsets of a given dataset. Then, it calculates the average for strengthening the predictive accuracy of a dataset.
Time Series (Introduction)
Time-Series Analysis consists of methods to analyze data on time-series, which later extracts meaningful statistics and other information. Time-Series forecasting is a method to predict future values by taking the help of previously observed values.

Week 7

This week will take you beyond traditional ML into the realm of Neural Nets and Deep Learning. You’ll learn how Deep Learning can be successfully applied to areas such as Computer Vision, and more.

Module 5: Deep Learning

1 Project

The next module in this applied Data Science course is Deep Learning. Deep Learning is an application of Machine Learning and Artificial Intelligence.

Intro to Neural Networks
Neural networks are inspired by the human brain, which is used to extract deep/high-level information from the raw input, like images, objects, etc. This chapter introduces you to artificial neural networks in deep learning.
Convolutional Neural Networks
Convolutional Neural Networks (CNN) are used for image processing, segmentation, classification, and several other applications. This chapter helps you learn all the essential concepts about CNN.
Transformers
Transformers are a recent, very successful neural network architecture that applies to language, graphs, and images. You will learn the basics of this architecture and see how it can be applied to different types of data.

Week 8

Learn about the different types of recommendation engines, how they are produced, and their specific applications to business use-cases.

Module 6: Recommendations Systems

1 Project

The next module in this MIT Professional Education Applied Data Science Program will teach you about implementing recommendation systems.

Intro to Recommendation Systems
As the name implies, recommendation systems help you predict the future preference of some products, which later recommend you the best-suited items to customers.
This chapter will teach you how to use a recommendation system so that you can choose the best products for customers.
Matrix
In this chapter, you will learn about the matrix used in recommendation systems.
Tensor, NN for Recommendation Systems
In this chapter, you will learn how to implement Tensor and NN for recommendation systems.

Week 9 - Learning Break (with revision sessions)

Weeks 10-12

The final three weeks of the program are reserved for the Capstone Project, which will enable you to integrate your skills and learning from the previous modules to solve a focused business problem.

Module 7: Capstone Project

The last module is capstone project, you will implement a hands-on capstone projects to master Data Science.

Week 10: Milestone
In week 10, you will implement the foundations of your capstone project related to data science.
Week 11: Final Submission
In week 11, you will work toward submitting the capstone project related to data science.
Week 12: Synthesis + Presentation
In week 12, you will be reviewed on the projects implemented with synthesis and presentation.

Self-Paced Modules

Module 1 - Demystifying ChatGPT and Applications

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

Certificate of Completion from MIT Professional Education and 16 Continuing Education Units (CEUs)

Earn a professional certificate of completion in Applied Data Science from the Massachusetts Institute of Technology (MIT) Professional Education. This program’s comprehensive and exhaustive curriculum nurtures you into a highly skilled professional in Applied Data Science, which later helps you land a job at the leading organizations worldwide.

Languages and Tools covered

Hands-on Projects for Data Science Training

Following a learn-by-doing pedagogy, the Applied Data Science Program: Leveraging AI for Effective Decision-Making offers you the opportunity to apply your skills and knowledge in real-time. Each learner mandatorily needs to submit 3 projects that include a Project for the first course : Foundations - Python and Statistics, 1 Project of their choice out of the 5 projects associated with core courses taught by MIT Faculty, and a 3-week capstone project.

Below are samples of potential project topics :

Marketing

Capstone - Marketing Campaign Customer Segmentation

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BFSI

Capstone - Loan Default Prediction

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Healthcare

Capstone - Malaria Detection

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Research

Capstone - Emotional Detection - DL CNN

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Entertainment

Capstone - Music Recommendation Systems

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Transportation

Capstone - Used Card Price Prediction

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Retail

Amazon AI Product Recommendation System

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Healthcare

Diabetes Analysis

Learn more

Real Estate

AI-Powered Boston House Price Prediction

Learn more

Education

Predicting Potential Customers

Learn more

Meet your MIT Faculty and Industry Mentors

Benefit from the extensive expertise of renowned Data Science and Machine Learning faculty from MIT, as well as seasoned data science practitioners from prominent global organizations.

Program Faculty

Devavrat Shah

Professor, EECS and IDSS, MIT

Munther Dahleh

Program Faculty Director, MIT Institute for Data, Systems, and Society (IDSS)

Caroline Uhler

Henry L. & Grace Doherty Associate Professor, EECS and IDSS, MIT

John N. Tsitsiklis

Clarence J. Lebel Professor, Dept. of Electrical Engineering & Computer Science (EECS) at MIT

Stefanie Jegelka

X-Consortium Career Development Associate Professor, EECS and IDSS, MIT

Note: This is an indicative list and is subject to change based on the availability of faculty

Industry Mentors from top-organizations

Fahad Akbar

Senior Manager Data Science

Bain & Company

Udit Mehrotra

Data Science Specialist

McKinsey & Company

Shannon Schlueter

Director of Data Science

Zwift

Vaibhav Verdhan

Analytics Leader, Global Advanced Analytics

Marco De Virgilis

Actuarial Data Scientist Manager

Arch Insurance Group Inc.

Note: This is an indicative list and is subject to change based on the availability of mentors

Your Learning Experience

The Applied Data Science Program: Leveraging AI for Effective Decision-Making is distinguished by its unique combination of MIT's academic leadership, live virtual sessions by MIT faculty, an application-based pedagogy, and personalised mentorship from industry experts.

STRUCTURED LEARNING JOURNEY

Learn from World-class Faculty

Live weekly virtual sessions with the MIT faculty in Data Science & Machine Learning
Program curriculum and design by award-winning MIT faculty
Understand the intricacies of Data Science techniques and their applications to real-world problems

PERSONALIZED AND INTERACTIVE

Mentorship and Program Support

Weekly online mentorship from Data Science and AI experts
Small groups of learners for personalized guidance and support
Interaction with like-minded peers from diverse backgrounds and geographies
Get personalized assistance with a dedicated Program Manager

View Experience

PRACTICAL AND HANDS-ON

Get Dedicated Career Support and Build an e-portfolio

1-on-1 Career Sessions: Interact with industry professionals in personal session to get insights on industry and career guidance
Resume & Linkedin Profile Review: Present yourself in the best light through a profile that showcases your strengths
E - Portfolio: Build an industry-ready portfolio to showcase your mastery of skills

Why Our Learners Choose the Applied Data Science Program: Leveraging AI for Effective Decision-Making

Thank you for the great lessons. MIT Live Lectures and MLS were equally beneficial. I learned about Machine Learning and the various models that we got to implement for our future endeavours in this exciting discipline.

Benjamin Choi

Site Reliability Engineer, Microsoft (USA)

This program is very well paced and gives you the right results in a relatively short period of time. The faculty is naturally top-notch and you expect nothing less given they are MIT professors. The lectures themselves were well-structured and very much to the point.

Ivan Strugatsky

Portfolio Manager, Stran Capital (USA)

I can safely say that this course is worth every penny and more for data science professionals. The course is accessible through a combination of live virtual classes with world-class MIT lecturers, and weekend mentored learning sessions with current industry professionals. It promises high-quality of education in a compact delivery portal, which is convenient for working professionals.

Brooks Christensen

DevOps Engineer, Nielsen

Thank you so much for an incredible experience! My confidence, competence and conviction in data science has transformed! A special thank you to the Program Office for curating an incredible learning experience, one that exceeded all my expectations and gave me the rigor, insights and practical skills I was looking for.

Jamal Madni

Co-founder and CEO, Ingage.Solutions (USA)

The adeptness, simplification and succinct explanation of concepts by the MIT professors was simplified yet detail oriented with examples and simple numerical illustrations. I continue to watch / refer to the recorded video lectures for clarifications of concepts. The capstone project allowed me to dive deeper into the CNN modelling, and the nitty-gritties of model evaluations and performance as well as condensing the outcomes to be presented from a business perspective.

Chenchal Subraveti

Sr. Research Analyst, Vanderbilt University (USA)

Learner Testimonials

As a busy working professional, I’m incredibly thankful for the flexibility this program offered without diminishing the content and experience of hands-on learning. My program manager was responsive and empathetic and would recommend the program to any aspiring data science professional.

Tanya Johnson

Customer Engineering Manager at Google

The attention to detail in every aspect of the program was amazing. Although the pace and rigor of the course was intense, I felt supported along every aspect of the journey.

Adrian Mendoza

Director, UX Strategy & Design at Deloitte

The data science program from Great Learning was highly organized as compared to other platforms, and the level of engagement from mentors was astonishing. The program coordinator was also very supportive throughout.

Khashayar Ebrahimi

Senior Engineer - Solver Developer at Gamma Technologies

Delivered by industry-leading faculty, the lectures provide a good amount of breadth and depth. The mentored learning sessions and capstone projects compound the way in which you learn.

Chad Barrett

Insights Analyst at Equinix

A wonderfully intense, engaging, and hands-on learning experience! The lecturers were top-notch, as were the mentors. The learning format allows you to apply data science concepts across a variety of cases. The program team was very helpful and attentive to our requests.

Wasyl Baluta

CEO/CTO at Plexina Inc.

There is great thought put into how the program is structured, who are the faculty members and mentors, what are the evaluation mechanisms to make sure we are building upon the knowledge that was gained.

Pradeep Podila

Health Scientist- Senior Service Fellow at CDC

The lectures from MIT faculty are great and the mentors provide a lot of guidance throughout the program. It was such a great experience.

Kalpana Vetcha

QA Portfolio Manager at Retail Business Services, an Ahold Delhaize Company

The program was very rewarding. The content from MIT faculty and the program design was engaging and of high quality. Peer interaction and review sessions from mentors helped us to define and solve various business cases at our own pace.

Sabina Sujecka

Software Expert UX Designer at Orange

The structure of the program is perfectly designed with working professionals in mind. MIT faculty provided a great understanding of the concepts, and the mentored learning sessions from Great Learning gave real industry insights that are directly translatable to the workforce.

Arman Seuylemezian

Research Scientist at Jet Propulsion Laboratory

I want to thank the mentors, MIT professors, teaching assistants, and everyone who made the program run smoothly. I now feel more confident in exploring data and implementing ML models. My mentor did an excellent job providing more context to concepts and going through examples.

Matthew Wolf

Postdoctoral Researcher at University of Guelph

I believe MIT PE has one of the best data science programs out there. It is aptly designed in terms of duration and content covered to train someone as a future Data Scientist. It was also insightful, learning from some of the best faculty members.

Abhishek M.

Principal Data Scientist at Nielsen

Learner Feedback on Mentorship and PM Support

Applied Data Science Program

Learner Satisfaction Rating

9,576Ratings

All Reviews

Wasyl Baluta

Batch of May 2022 | ceo at plexina inc (formerly wairever inc) | canada

With 25 years of experience as a technology leader, I took up this program to expand our company's services portfolio with analytics and machine learning capabilities. The program provided quality material and practical case studies, which helped me understand the concepts quickly and build working predictive models. The curriculum was well-organized, and the key takeaways for me were the steps of any Data Science project, how to perform them, identify relevant techniques, assess data quality, construct different kinds of predictive models, tune them, and use industry-standard tools such as Jupyter, DataSpell, and Google Colab.

31 Jan 2024

Andrea Robles

Batch of May 2023 | srmanager credit models&data analysis at scotiabank | costa rica

Completing the MIT Applied Data Science Program was pivotal for me. With a background in Statistics and Mathematics, I sought to refresh my skills and stay current in Data Science. The program's intensity pushed me to excel, and its intuitive platform and responsive support made the journey seamless. I mastered Python coding for data analysis, visualization, and Machine Learning, enhancing my capabilities as a Data Scientist. Now, equipped with deeper insights into ML and data-driven decision-making, I confidently contribute to projects as a Product Owner, bridging technical expertise with stakeholder needs.

02 Apr 2024

Llana Grossman

Batch of March 2023 | Product Manager, Co-Founder at Bit Discovery | United States

Transitioning from a Product Manager to a tech entrepreneur, I've prioritized rounding out my tech expertise. Attending diverse professional bootcamps and programs has been integral. MIT's Applied Data Science Program stood out for its rigor and value. Taught by top MIT professors, it's complemented by exceptional support from Great Learning. Highly recommend this program for anyone aiming to strengthen their tech skills while pursuing entrepreneurial ventures.

02 Apr 2024

Adriana D'Oporto

Batch of March 2023 | Senior Pricing Systems Analyst at Cargill | United States

Completing the MIT Applied Data Science program has been truly transformative. It's a pivotal step forward in my career as a pricing analyst, equipping me with invaluable skills and insights. The program's depth of content, coupled with engaging lectures and hands-on projects, has opened up a world of possibilities. Special thanks to the mentors whose unwavering support made this journey exceptional.

02 Apr 2024

Egzona Morina

Batch of March 2023 | phd candidate at university college london | united states

I enrolled in the MIT Applied Data Science program to expand my skill set and it turned out to be the perfect program for me. The staff was very responsive and supportive. The syllabus was amazing. Overall, I had a very positive experience and I would highly recommend it.

02 Apr 2024

Luis E Angulo

Batch of March 2023 | Sr IT Consultant ( Owner) at Sjln consulting LLC | United States

MIT's Applied Data Science Program added depth to my skill set. Witnessing data's pivotal role in Telecom, I was driven to understand, analyze, and predict its impact. The program's intensity ignited my curiosity, amplifying my Data Science mindset. Python proficiency and critical thinking are now my tools. Though it's early to gauge, I anticipate leveraging these skills to enhance project portfolios and deliver heightened value to clients. A big thanks to the Great Learning team for enabling this transformative journey.

02 Apr 2024

Gayathri Varma

Batch of March 2023 | masters student at university of arizona | united states

Great Learning provides an excellent program experience. Transitioning from academia, I initially hesitated, but the program advisor’s reassurance eased my concerns. Prework in Basic Statistics and Python was invaluable, especially as a programming novice. MIT-led live virtual classes, facilitated by mentors, were enriching, followed by comprehensive post-class materials. The highlights of the MIT Applied Data Science program include robust learning resources, mentored sessions, and responsive support. Overall, it's been a rewarding journey.

02 Apr 2024

Iryna Galenko

Batch of May 2023 | program finance analyst at crdf global | united states

I started the program with zero experience in Python and Data Science, unsure if I could succeed. However, after three months of dedicated learning, I now confidently handle technical aspects, analyze data, and present findings with recommendations. The pre-work materials provided a solid foundation, crucial for a beginner like me. The lectures by MIT professors were captivating, and mentor support was invaluable in bridging theory and practice. The program manager's consistent support made the online experience feel connected and personal. Overall, it's been an amazing journey with the MIT applied data science program, inspiring me to contribute meaningfully to this innovative field.

02 Apr 2024

Nguyen Huy Anh

Batch of September 2022 | Lecturer at Hanoi School of Business - Vietnam University | Vietnam

The MIT Applied Data Science program helped me upgrade my knowledge and keep up with hot-trend technology and machine learning algorithms, and improve my coding skills with Python language. World-class teaching from MIT professors, a high-quality project with a clear algorithm and purpose for each module, and 24/7 support from the Program Manager made the program worth every bit of my time.

02 Apr 2024

Alireza Sharifikia

Batch of March 2023 | technical lead at corpy&co. | japan

I'm immensely grateful for the transformative journey I've had in the MIT Applied Data Science program over 12 weeks. Starting with Python basics, we delved into Data Science essentials, statistical analysis, and machine learning. The support from faculty members and flexible project options made learning enjoyable. From deep learning to AI applications like Computer Vision and Generative AI, each module expanded my horizons. I can't thank MIT and Great Learning enough for this invaluable experience.

02 Apr 2024

Read all reviews

Program Fees

Program Fees: 3,900 USD

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*Subject to partner approval based on applicable regions & eligibility.

Benefits of learning from us

Live virtual sessions from MIT Faculty
Live mentorship from Data Science and AI experts
2 Self-paced modules on ChatGPT and Generative AI
Personalized assistance from a dedicated Program Manager
Access dedicated support to enable career growth
Build an industry-ready portfolio to showcase your mastery of Data Science skills

Application Process

Fill the Application Form

Application Screening

Your application will be reviewed by a panel from Great Learning to determine if it is a fit with the program

Join the Program

After a final review, you will receive an offer for a seat in the upcoming cohort of the program.

Upcoming Application Deadline

Admissions are closed once the requisite number of participants enroll for the upcoming cohort . Apply early to secure your seat.

Deadline: 17^th Apr 2025

Reach out to us

We hope you had a good experience with us. If you haven’t received a satisfactory response to your queries or have any other issue to address, please email us at

help@mygreatlearning.com

Cohort Start Date

Live Virtual

17^th May 2025

Still have queries? Contact Us

Please fill in the form and a Program Advisor from Great Learning will reach out to you. You can also reach out to us at adsp.mit@mygreatlearning.com or +1 617 468 7899

Application Closes 17^th Apr 2025

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Check out the program and fee details in our brochure

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Delivered in Collaboration with:

MIT Professional Education is collaborating with online education provider Great Learning to offer Applied Data Science Program: Leveraging AI for Effective Decision-Making. 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

MIT Professional Education's Data Science Course

Applied Data Science Program: Leveraging AI for Effective Decision-Making

Become a data-driven decision maker. Learn applied Data Science, AI, and Machine Learning with live virtual sessions from MIT Faculty and immersive hands-on projects.

Contact Us: +1 617 468 7899

Why Join the Applied Data Science Program: Leveraging AI for Effective Decision-Making

Learn from Top Faculty

Mentorship and Program Support

Practical, Hands-on Training

Live Virtual Sessions from world-renowned MIT Faculty

Personalised mentorship and guidance from data science practitioners

Hands-on training via 2 projects and 1 Capstone

Curriculum covering Deep Learning, Machine Learning, with Python

Applied Data Science Program for Working Professionals

Certificate of Completion from MIT Professional Education

#1

MIT's Rank in World Universities

#2

MIT's Rank in National Universities

Curriculum Designed for Professionals

Languages and Tools covered

Hands-on Projects for Data Science Training

Capstone - Marketing Campaign Customer Segmentation

Capstone - Loan Default Prediction

Capstone - Malaria Detection

Capstone - Emotional Detection - DL CNN

Capstone - Music Recommendation Systems

Capstone - Used Card Price Prediction

Amazon AI Product Recommendation System

Diabetes Analysis

AI-Powered Boston House Price Prediction

Predicting Potential Customers

Meet your MIT Faculty and Industry Mentors

Your Learning Experience

Learn from World-class Faculty

Mentorship and Program Support

Get Dedicated Career Support and Build an e-portfolio

Why Our Learners Choose the Applied Data Science Program: Leveraging AI for Effective Decision-Making

Benjamin Choi

Ivan Strugatsky

Brooks Christensen

Jamal Madni

Chenchal Subraveti

Learner Testimonials

Tanya Johnson

Adrian Mendoza

Khashayar Ebrahimi

Chad Barrett

Wasyl Baluta

Pradeep Podila

Kalpana Vetcha

Sabina Sujecka

Arman Seuylemezian

Matthew Wolf

Abhishek M.

Learner Feedback on Mentorship and PM Support

Program Fees

Benefits of learning from us

Application Process

Fill the Application Form

Application Screening

Join the Program

Upcoming Application Deadline

Deadline: 17th Apr 2025

Reach out to us

Cohort Start Date

Live Virtual

Still have queries? Contact Us

Download Brochure

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Deadline: 17^th Apr 2025