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Applied AI and Data Science Program

Applied AI and Data Science Program

Application closes 15th Oct 2025

Distinctive features

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    Low-code approach

    Build AI and data science workflows with minimal coding using intuitive tools. Perfect for professionals looking to advance their proficiency in AI without deep programming experience.

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    GenAI-infused curriculum

    Covers the latest in Generative AI: Transformers, RAG, Prompt Engineering, and Agentic AI. Designed for real-world business applications.

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Unlock real-world impact

Elevate your career in AI and data science

Build your AI and data science proficiency with the latest GenAI tools.

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    Apply AI and data science to solve real-world business problems

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    Learn to apply techniques across domains such as NLP, GenAI, Computer Vision, and Recommendation Systems.

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    Learn effective data representation for predictive modeling

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    Create an industry-ready ePortfolio

Earn a certificate of completion from MIT Professional Education

  • ranking 1

    #1 in World Universities

    QS World University Rankings, 2025

  • ranking 1

    #1 in AI and Data Science

    QS World University Rankings by Subject, 2025

  • ranking 2

    #2 in National Universities

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

Key program highlights

Why choose the Applied AI and Data Science Program

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    Live online sessions with MIT faculty

    Engage in live online sessions with renowned MIT faculty for interactive insights.

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    Low-code approach

    Build AI and data science skills using low-code tools and techniques, enabling hands-on learning without heavy coding.

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    Latest AI tech stack

    Explore the latest Generative AI models, including Prompt Engineering and RAG modules.

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    Personalized mentorship by experts

    Benefit from weekly online mentorship from Data Science and AI industry experts.

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    Build an industry portfolio

    Work on 50+ case studies, projects, and a capstone project solving real business problems with AI.

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    Earn a globally recognized credential

    Earn 16 CEUs and a certificate of completion from MIT Professional Education upon completion.

Skills you will learn

PYTHON

DATA ANALYSIS

DATA VISUALIZATION

MACHINE LEARNING

ARTIFICIAL INTELLIGENCE

COMPUTER VISION

DEEP LEARNING

GENERATIVE AI & PROMPT ENGINEERING

RETRIEVAL AUGMENTED GENERATION

AGENTIC AND ETHICAL AI

PYTHON

DATA ANALYSIS

DATA VISUALIZATION

MACHINE LEARNING

ARTIFICIAL INTELLIGENCE

COMPUTER VISION

DEEP LEARNING

GENERATIVE AI & PROMPT ENGINEERING

RETRIEVAL AUGMENTED GENERATION

AGENTIC AND ETHICAL AI

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Build your AI and data science proficiency

  • 86% Execs Report

    AI critical to firms

  • 11.5 Mn+

    Jobs in data by 2026

  • 69% Global Leaders

    Say AI #1 for growth

  • $103.5 Bn

    Analytics market size

  • Overview
  • Learning Journey
  • Curriculum
  • Projects
  • Tools
  • Certificate
  • Faculty
  • Mentors
  • Career support
  • Fees
  • FAQ
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This program is ideal for

Data professionals and managers seeking AI-driven insights

  • Extracting Insights from Data

    Professionals seeking to uncover patterns, extract actionable insights from large data sets, and build robust AI and Data Science solutions.

  • Driving Strategic Impact

    Professionals aiming to leverage AI and data science for business strategies, improve decision-making, and lead AI and Generative AI initiatives.

  • Building AI Expertise

    Those interested in strengthening their understanding of AI, generative AI, and machine learning through hands-on projects and expert-led learning.

  • Deepening Technical Skills

    Professionals with a background in technical management, business intelligence analysis, data science management, IT, management consulting, or business management, including data science and AI enthusiasts.

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.

  • Low-Code

    Approach

  • Live Online Sessions

    by MIT Faculty

  • 10+

    Emerging Tools and Technologies

Self-Paced Module | Ethical and Responsible AI

In this module, you will delve into the critical ethical considerations that underpin the entire AI lifecycle through practical insights and real-world examples. 

  • Introduction to AI Lifecycle

  • Introduction to Bias and Its Examples

  • Introduction to Causality and Privacy

  • Interconnections and Domains

  • Interdependency and Feedback in AI Systems

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 of AI

In this module, you will establish the essential programming and statistical foundations crucial for your journey in data science using:

  • Python for Data Science(NumPy & Pandas)
  • Python for Visualization
  • Inferential Statistics
  • Hypothesis Testing

Week 3: Data Analysis and Visualization

In this module, you will learn hypothesis testing, dimensionality reduction, network analysis, and various clustering algorithms with practical applications.

  • Hypothesis testing and practical applications
  • Dimensionality reduction using PCA and t-SNE
  • Network Analysis
  • Different types of clustering algorithms

Week 4: Machine Learning

In this module, you will build foundational machine learning models and understand their evaluation.

  • Maximum Likelihood, Bayesian Estimators & formulation
  • Linear Regression & Assumptions
  • Cross-validation & Bootstrapping
  • Classification using Logistic Regression & KNN)
  • Gaussian Models

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 Decision Tree
  • Entropy & Information Gain
  • Ensemble Learning - Bagging, Bootstrapping, and Random Forests
  • Time Series Forecasting

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
  • Filters/Convolutions, Pooling, and Max-Pooling
  • Architecture of CNN
  • Transfer Learning and Augmentation
  • Encoder Decoder Architecture
  • Token-based Processing, Attention Mechanism & Positional Encodings

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.

Work on hands-on projects and case studies

Engage in practical projects and program-specific case studies using emerging tools and technologies across sectors

  • 50+

    Case Studies

  • 2 Projects

    Industry-Relevant

  • Capstone Project

    Hands-on Learning

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Healthcare

Brain Tumor Image Classifier

About the case study

This case study involves building a binary classification model to detect Pituitary Tumors in MRI scans. Learners work with a dataset of 1,000 images (830 for training, 170 for testing), implementing data augmentation to reduce overfitting. Using transfer learning with pre-trained CNN models, learners improve classification accuracy for medical imaging tasks.

Concepts used

  • Image Classification
  • Data Augmentation
  • Transfer Learning
  • Pre-trained Models
  • Convolutional Neural Networks (CNN)
  • Python Programming
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Asset Management

Network Stock Portfolio Optimization

About the case study

In this case study, learners use network analysis and clustering techniques to construct optimized stock portfolios aimed at outperforming market indices like the S&P 500. By simulating portfolio performance and evaluating relative returns, this case empowers learners to develop intelligent, data-backed investment strategies.

Concepts used

  • Network Analysis
  • Portfolio Construction
  • Stock Selection
  • Clustering Approaches
  • Simulation Techniques
  • Python Programming
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Hospitality

Hotel Booking Cancellation Prediction

About the case study

Learners develop a predictive model to identify likely hotel booking cancellations and no-shows. Using customer and booking data, the model improves resource allocation and revenue management. This case focuses on logistic regression, decision trees, and visualization to derive actionable insights for hospitality management.

Concepts used

  • Exploratory Data Analysis
  • Data Preprocessing
  • Feature Engineering
  • Logistic Regression
  • Decision Trees
  • Random Forests
  • Python Programming
  • Data Visualization
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Entertainment

Movie Recommendation Systems

About the case study

This case study challenges learners to build recommendation engines that enhance user experience on streaming platforms. Using collaborative filtering and matrix factorization, learners develop personalized movie suggestions based on historical interactions, helping boost engagement and satisfaction.

Concepts used

  • Exploratory Data Analysis
  • Data Preprocessing
  • Knowledge/Rank-Based Recommendations
  • Similarity-Based Collaborative Filtering
  • Matrix Factorization
  • Python Programming
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Marketing

Marketing Campaign Analysis

Description

This project focuses on performing customer segmentation to improve the effectiveness of marketing campaigns. Learners apply dimensionality reduction techniques like PCA and t-SNE, along with clustering algorithms such as K-Means and K-Medoids, to uncover meaningful patterns in customer behavior. The insights gained help drive data-informed strategies and improve customer engagement.

Skills you will learn

  • Python
  • Exploratory Data Analysis
  • Data Pre-Processing
  • Dimensionality Reduction (PCA, t-SNE)
  • Clustering (K-Means, K-Medoids)
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Automotive

Used Car Price Prediction

Description

In this regression-based project, learners build predictive models to estimate used car prices using features such as make, model, year, and mileage. Emphasis is placed on feature engineering, model selection, and evaluation to help stakeholders make accurate pricing decisions in the automotive resale market.

Concepts used

  • Python
  • Exploratory Data Analysis
  • Data Preprocessing
  • Feature Engineering
  • Regression Techniques
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Finance

Loan Default Prediction

Description

This project enables learners to develop a classification model that predicts the probability of a loan default. By analyzing customer demographics, financial history, and loan characteristics, participants apply classification algorithms and model evaluation techniques to support more effective credit risk management.

Concepts used

  • Python
  • Data Cleaning
  • Exploratory Data Analysis (EDA)
  • Classification Algorithms
  • Model Evaluation Metrics
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Healthcare

Malaria Detection

Description

Learners use Convolutional Neural Networks (CNNs) and transfer learning to classify cell images as infected or uninfected with malaria. The project includes image preprocessing, model training, and interpretation, helping learners apply deep learning to critical healthcare diagnostics.

Concepts used

  • Python
  • Convolutional Neural Networks (CNNs)
  • Transfer Learning
  • Image Preprocessing
  • Model Training and Evaluation
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Technology

Facial Emotion Detection

Description

This project involves building a deep learning model to classify emotional states from facial images. By applying transfer learning with pre-trained models like VGG or ResNet, learners gain experience in emotion recognition, computer vision, and image-based AI applications.

Concepts used

  • Python
  • Deep Learning
  • Transfer Learning
  • VGG
  • ResNet
  • Grayscale Image Processing
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Music & Entertainment

Music Recommendation System

Description

Learners design a personalized recommendation engine using collaborative filtering, content-based filtering, and hybrid techniques. By analyzing user behavior and music interaction data, the system delivers tailored music suggestions to improve user satisfaction and engagement.

Concepts used

  • Python
  • Exploratory Data Analysis
  • Data Pre-Processing
  • Collaborative Filtering
  • Content-Based Filtering
  • Hybrid Recommendation Systems
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Retail

Generative AI-Powered Customer Review Categorization

Description

In this project, learners apply Generative AI to classify and summarize customer feedback. Using techniques such as Retrieval Augmented Generation (RAG) and sentiment analysis, the project helps organizations extract actionable insights to improve customer experience and product strategy.

Concepts used

  • Retrieval Augmented Generation (RAG)
  • Sentiment Analysis
  • Labeling
  • Summarization

Master in-demand AI and Data Science tools

Benefit from hands-on experience with 10+ top AI and Data Science low-code tools

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    Python

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    NumPy

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    Pandas

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    Tensorflow

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    Transformers

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    Seaborn

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    Scikit-Learn

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    Keras

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    OpenCV

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    LangChain

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    Hugging Face

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    ChatGPT

  • tools-icon

    Dalle

  • And More...

Earn a Professional Certificate in Applied AI & Data Science

Get a certificate of completion from MIT Professional Education and showcase it to your network

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* Image for illustration only. Certificate subject to change.

Learn from MIT faculty

  • Caroline Uhler - Faculty Director

    Caroline Uhler

    Professor, EECS and IDSS

    Expert in computational biology, statistics, and systems.

    Award-winning scholar relentlessly driving transformative data insights.

    Know More
  • John N. Tsitsiklis - Faculty Director

    John N. Tsitsiklis

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

    Leader in optimization, control, and learning.

    Renowned scholar with multiple prestigious accolades.

    Know More
  • Munther Dahleh - Faculty Director

    Munther Dahleh

    William A. Coolidge Professor, EECS and IDSS; Founding Director, IDSS

    Trailblazer in robust control and computational design.

    Director propelling interdisciplinary research and innovation.

    Know More
  • Stefanie Jegelka - Faculty Director

    Stefanie Jegelka

    Associate Professor, EECS and IDSS

    Expert in algorithms and optimization for AI.

    Pioneer advancing theoretical machine learning foundations.

    Know More
  • Devavrat Shah - Faculty Director

    Devavrat Shah

    Andrew (1956) and Erna Viterbi Professor, EECS and IDSS

    Renowned expert in large-scale network inference.

    Award-winning innovator in data-driven decisions.

    Know More

Interact with our mentors

Interact with dedicated and experienced AI and data science experts who will guide you through your learning journey

  •  Omar Attia - Mentor

    Omar Attia

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

    Bhaskarjit Sarmah linkin icon

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

    Vibhor Kaushik

    Data Scientist Amazon
    Amazon Logo
  •  Matt Nickens - Mentor

    Matt Nickens

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

    Nirmal Budhathoki

    Senior Data Scientist Microsoft
    Microsoft Logo
  •  Mohit Khakaria  - Mentor

    Mohit Khakaria

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

    Udit Mehrotra

    Senior Data Scientist Google
    Google Logo
  •  Vaibhav Verdhan - Mentor

    Vaibhav Verdhan

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

    Amish Suchak

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

    Nirupam Sharma

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

    Deepa Krishnamurthy

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

    Marco De Virgilis

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

    Cristiano Santos De Aguiar

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

    Saber Fallahpour

    Principal Data Scientist Altair
    Altair Logo
  •  Asim Sultan  - Mentor

    Asim Sultan

    Senior Machine Learning Engineer RiskHorizon AI
    RiskHorizon AI Logo

Watch inspiring success stories

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    Watch story

    "MIT faculty are some of the best teachers I have ever had"

    MIT faculty explain everything from the very basic theory of every machine learning algorithm to the toughest concepts. Mentors let you see the practical side of things too. This is the course every student who wants to get into data science should take.

    Mauricio De Garay

    Student ,

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    "From Day 1 was able to solve meaningful, real world, tangible problems"

    I had a great experience with world-class instructors and live classes led by industry experts who clearly explained each concept. I highly recommend this program to anyone considering a career shift.

    Brooks Christensen

    DevOps Engineer , Nielsen

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    "The course was seemless and very engaging"

    I enrolled to refresh my technical knowledge, and the projects were highly relevant to real-life scenarios. The mentor was an exceptional coder who explained concepts clearly, and the neural networks sessions were both engaging and fun with great examples.

    Gabriela Alessio Robles

    Senior Analytics Engineer , Netflix

Get industry-ready with dedicated career support

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    Get dedicated career support

    Access personalized guidance to strengthen your professional brand.

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    1-on-1 career sessions

    Interact with industry professionals to gain actionable career insights.

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    Resume & LinkedIn profile review

    Showcase your strengths with a polished, market-ready profile

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    Build your project portfolio

    Build an industry-ready portfolio to showcase your skills

Course fees

The course fee is 3,900 USD

Advance your career

  • benifits-icon

    Apply AI and data science to solve real-world business problems

  • benifits-icon

    Learn to apply techniques across domains such as NLP, GenAI, Computer Vision, and Recommendation Systems.

  • benifits-icon

    Learn effective data representation for predictive modeling

  • benifits-icon

    Create an industry-ready ePortfolio

Take the next step

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Apply to the program now or schedule a call with a program advisor

Unlock exclusive course sneak peek

Application Closes: 15th Oct 2025

Application Closes: 15th Oct 2025

Talk to our advisor for offers & course details

Registration process

Our registrations close once the requisite number of participants enroll for the upcoming batch. Apply early to secure your seat.

  • steps icon

    1. Fill application form

    Register by completing the online application form.

  • steps icon

    2. Application screening

    A panel from Great Learning will assess your application based on academics, work experience, and motivation.

  • steps icon

    3. Join program

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

Eligibility

  • Exposure to computer programming and a high school-level knowledge of Statistics and Mathematics

Batch start date

  • Online · 15th Nov 2025

    Admission closing soon

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

 

Got more questions? Talk to us

Connect with a program advisor and get your queries resolved

Speak with our expert +1 617 468 7899 or email to aaidsp.mit@mygreatlearning.com

career guidance

Delivered in Collaboration with:

MIT Professional Education is collaborating with online education provider Great Learning to offer Applied AI and Data Science Program. 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

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