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

Transform into 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.

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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

Live Virtual Teaching by
MIT Faculty
  • Live Virtual Sessions from world-renowned MIT Faculty
  • Curriculum designed to build industry-valued skills: Deep Learning, Machine Learning with Python.
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Personalized Mentorship and Support
  • Live mentorship and guidance from data science practitioners on weekends
  • Collaborative yet personalized sessions in small groups
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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.
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Working Professional
Live Virtual Sessions from world-renowned MIT Faculty
Career Support
Personalised mentorship and guidance from data science practitioners
Salary Hike
Hands-on training via 2 projects and 1 Capstone
Industry Project
Curriculum covering Deep Learning, Machine Learning, with Python

Applied Data Science Program for Working Professionals

Live Virtual Sessions by MIT Program Faculty | Mentorship from Experts | 12 Weeks

Certificate of Completion from MIT Professional Education

MIT bootcamp certificate
QS World

World #1

MIT Rank in World Universities

QS World University Rankings, 2023

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U.S #2

MIT Rank in National Universities

U.S News & World Report Rankings, 2022

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.

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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 for Data Science
Project Icon 1 Project
    Project Icon 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
Project Icon 1 Project
    Project Icon 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
Project Icon 1 Project
    Project Icon 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
Project Icon 1 Project
    Project Icon 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
Project Icon 1 Project
    Project Icon 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
Project Icon 1 Project
    Project Icon 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 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.

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 for Data Science, 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

Using Unsupervised Learning ideas such as Dimensionality Reduction and Clustering, the objective is to come up with the best possible customer segments using the given customer dataset.
Tools & Concepts: K-Means, DBSCAN, GMM, Hierarchical Clustering, K-Mediods, PCA, T-SNE
Learn more

BFSI

Capstone - Loan Default Prediction

Build a classification model to predict clients who are likely to default on their loan and give recommendations to the bank on the important features to consider while approving a loan.
Tools & Concepts: Logistic Regression, Decision Trees, Random Forests, Ensemble Methods
Learn more

Healthcare

Capstone - Malaria Detection

Build an efficient computer vision model to detect malaria. The model should identify whether the image of a red blood cell is that of one infected with malaria or not, and classify the same as parasitized or uninfected, respectively.
Tools & Concepts: Artificial Neural Networks, Convolution Neural Networks, Computer Vision, Transfer Learning, CNN Regularizatio
Learn more

Research

Capstone - Facial emotion detection - DL CNN

The goal of this project is to use Deep Learning and Artificial Intelligence techniques to create a computer vision model that can accurately detect facial emotions. The model should be able to perform multi-class classification on images of facial expressions, to classify the expressions according to the associated emotion.
Tools & Concepts: Artificial Neural Networks, Convolution Neural Networks, Computer Vision, Transfer Learning, CNN Regularization
Learn more

Entertainment

Capstone - Music Recommendation Systems

To recommend 10 songs to a user based on their likelihood of liking those songs.
Tools & Concepts: Rank-based, Similarity-based, collaborative filtering, content-based filtering, SVD-based models, and Matrix Factorization based Recommendation System
Learn more

Transportation

Capstone - Used Card Price Prediction

Explore and visualize the dataset. Build a model to predict the prices of used cars. Generate a set of insights and recommendations that will help the business.
Tools & Concepts: Linear Regression, Lasso, and Ridge regression, OLS, Ensemble Methods
Learn more

Retail

Amazon AI Product Recommendation System

This project involves recommending the best Amazon products available to users based on past rating data using AI-driven recommendation systems techniques.
Tools & Concepts: Rank-based, Similarity-based, Matrix Factorization-based, Content-based Recommendation Systems
Learn more

Healthcare

Diabetes Analysis

This project involves analyzing different aspects of Diabetes in the Pima Indians tribe.
Tools & Concepts: Exploratory Data Analysis, Data Visualization, and Statistics
Learn more

Real Estate

AI-Powered Boston House Price Prediction

This project involves predicting house prices in the Boston metropolitan area based on the features of the property and its locality using regression techniques.
Tools & Concepts: Linear Regression, Logistic Regression, and K-Nearest Neighbors
Learn more

Education

Predicting Potential Customers

This project involves identifying which leads are more likely to convert to paid customers based on attributes of leads and their interaction details.
Tools & Concepts: Decision Trees and Random Forests
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

Devavrat Shah

Professor, EECS and IDSS, MIT

Munther

Munther Dahleh

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

Caroline

Caroline Uhler

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

John N.

John N. Tsitsiklis

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

Stefanie

Stefanie Jegelka

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

Industry Mentors from top-organizations

Fahad

Fahad Akbar

Senior Manager Data Science

Bain & Company

Udit

Udit Mehrotra

Data Science Specialist

McKinsey & Company

Shannon

Shannon Schlueter

Director of Data Science

Zwift

Vaibhav

Vaibhav Verdhan

Analytics Leader, Global Advanced Analytics

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Marco De

Marco De Virgilis

Actuarial Data Scientist Manager

Arch Insurance Group Inc.

Your Learning Experience

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

STRUCTURED PROGRAM WITH LIVE VIRTUAL SESSIONS

Learn Data Science through Live Virtuals Sessions taught by MIT Faculty

  • Live weekly virtual sessions with the MIT faculty in Data Science & Machine Learning
  • Program curriculum and design by award-winning MIT faculty
  • Program which allows you to position yourself as a data science enabler by gaining industry-valued skills

PERSONALIZED AND INTERACTIVE

Personalised Mentorship and 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
  • Dedicated Program Manager provided by Great Learning, for academic and non-academic queries
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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

Tanya Johnson

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

Adrian Mendoza

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

Gabriela Alessio Robles

The program brushed up my technical skills. The mentors were fantastic and the weekend classes solidified the concepts learnt during the week.

Gabriela Alessio Robles

Senior Analytics Engineer at Netflix

Khashayar Ebrahimi

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

Chad Barrett

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

Wasyl Baluta

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.

Pradeep Podila

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

Kalpana Vetcha

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

Sabina Sujecka

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

Arman Seuylemezian

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

Matthew Wolf

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

Abhishek M.

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

Program Fees

Program Fees:
3,900 USD

Apply Now

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*Subject to partner approval based on regions & eligibility. dLocal for Brazil, Colombia & Mexico learners. Other partners for U.S. learners only.

Benefits of learning from us

  • Live Virtual Sessions from MIT Faculty
  • High-quality Content from MIT Faculty
  • Live Mentorship from Data Science and AI experts
  • 2 Self-paced modules on ChatGPT and Generative AI
  • Program Manager from Great Learning for Academic & Non-Academic Support
  • Get dedicated support to fuel your career transition

Application Process

1

Fill the Application Form

Register by completing the online application form.

2

Application Screening

Your application will be reviewed to determine if it is a fit with the program.

3

Join the Program

If selected, you will receive an offer for the upcoming cohort. Secure your seat by paying the fee.

Upcoming Application Deadline

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

Deadline: 30th Apr 2024

Apply Now
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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

Great Learning-Online Center

Live Virtual

18th May 2024

Frequently Asked Questions

Frequently Asked Questions
What is the duration of the Applied Data Science Program by MIT Professional Education?


The Applied Data Science program by MIT Professional Education is a 12-week live virtual program.

Who will teach this Data Science and Machine Learning program?


MIT faculty will deliver the live virtual sessions. Experienced program mentors will give students a practical understanding of core concepts through hands-on projects.

What are the prerequisites for enrolling in the program?

For this Applied Data Science Program, the applicant should have:
 

  • Exposure to Computer Programming languages and

  • High School- Level Knowledge of Statistics and Mathematics.

Can you provide details about the curriculum and course structure of MIT Professional Education's Applied Data Science Program?

The Applied Data Science Program syllabus is 12 weeks long. It consists of:
 

  • Foundation courses,

  • Core courses,

  • Project submissions,

  • Capstone projects, and

  • Self-paced modules on ChatGPT and Generative AI.

Will I receive a certificate upon completing the program?


Yes. After completing this Applied Data Science Program Certificate course, you will receive a certificate from MIT Professional Education. 

How is MIT Professional Education Applied Data Science Program different from other data science courses?

MIT Professional Education Applied Data Science Program program is different from other data science programs because of the following reasons:
 

  • It is offered by MIT Professional Education, an engineering and technology education leader for 70 years.

  • Learn from award-winning MIT faculty through live virtual sessions from the convenience of your home.

  • It helps you unravel the true worth of data through theoretical and practical learning.

  • Focus on Artificial Intelligence and ML projects and case studies to learn about utilizing AI for data decision-making.

  • Benefit from 1:1 career sessions, a resume, and LinkedIn review, an e-portfolio with hands-on projects, and capstone projects for practical learning.

  • Get live mentorship from industry experts on the applications of concepts taught by faculty.

  • Receive a certificate of completion from MIT Professional Education at the end of the program.

What kind of projects or case studies will be included in the program?

This Applied Data Science course will have capstone projects and projects in between modules for hands-on learning. These are some of the sample Hands-on and Capstone projects:

Sample hands-on projects

Healthcare

Malaria Detection

Detect whether Red Blood Cells (RBCs) are infected with malaria using the Image Classification technique.

Real Estate

AI-Powered Boston House Price Prediction

Predicting house prices in the Boston metropolitan area is based on the features of the property and its locality using Regression techniques.

Sample Capstone projects

BFSI

Loan Default Prediction

Build a classification model to predict clients who are likely to default on their loans. Give recommendations to the bank on important features to consider while approving a loan.

Research

Facial Emotion detection

Use Deep Learning and AI techniques to create a computer vision model that can accurately detect facial emotions. The model should be able to perform multi-class classification on the images of facial expressions and categorize them according to the associated emotion.

 

Who are the faculty members and instructors who will teach this Applied Data Science Program?

The program will be taught by MIT faculty who are academicians in fields like Data Science, Electrical Engineering, Computer Science, and more.

The program mentors are industry leaders and experts from leading companies like West. Jet, Apple, Amazon Web Services, IKEA, and more. These program mentors coach you to work on hands-on projects to apply theories to real-world challenges through live and personalized mentored learning sessions. This will help you use and analyze data in the real world and create data science skills. 

Note: Program faculty is subject to change.

What is the program fee for Applied Data Science program by MIT Professional Education?


The total program fee is USD 3900.

What is the application process for this course?

The program offers a simplified application process to follow:
 

Step 1: Fill out an online application form

Step 2: The application review will be done to determine your suitability for the program

Step 3: Join the program if your application is selected. Pay the fees and secure your seat for the upcoming cohort. 

Will I get any assistance during the program?


You will be getting program Manare, a personal guide for you who will assist you throughout the program. The program manager will be your sole point of contact, and they will monitor your progress and encourage you to succeed throughout the program.

What if my question is not covered here?

If you have any other questions, please contact Great Learning through:

Phone: +1 617 468 7899

email: adsp.mit@mygreatlearning.com

 

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 30th Apr 2024

Download Brochure

Check out the program and fee details in our brochure

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Delivered in Collaboration with:

MIT Professional Education's Applied Data Science Program: Leveraging AI for Effective Decision-Making, with a curriculum developed and taught by MIT faculty, is delivered in collaboration with Great Learning. Great Learning is an ed-tech company that has empowered learners from over 170+ countries in achieving positive outcomes for their career growth. Accessibility