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

Become a data-driven decision maker with live virtual teaching from MIT faculty, AI and ML-focused hands-on projects, and mentorship from industry practitioners.

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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: Machine Learning, Deep Learning, and Python.
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Personalized Mentorship and Support
  • Live mentorship and guidance from data science practitioners on weekends
  • Collaborative yet personalised sessions in small groups
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Practical, Hands-on Training
  • Complete hands-on exposure through 6 projects under the guidance of industry experts
  • Final 3-week Capstone Project on a real-world business problem
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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 Machine Learning, Deep Learning, and Python

Applied Data Science Program for 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

us news

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.

Syllabus designed for professionals

MIT Professional Education 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 1
      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

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

Healthcare

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: 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

MIT Faculty and Industry Experts

Learn from the vast knowledge of top MIT faculty in the field of Data Science and Machine Learning, along with experienced data science practitioners from leading global organisations.

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

Program Mentors

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

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 (USA)

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)

Ingage Solutions

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.

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

Ratings & Reviews by learners

All Reviews
07 May 2024
Batch of July 2022 | Senior Cloud Security Architect at Fidelity International | Hong Kong
This program offered me a comprehensive learning experience. The study materials, including live lectures, mentor sessions, and practical exercises, were amazing. This is the perfect program for beginners in Data Science. There are also study breaks in the middle of the program so that we can review what we have learned and ask questions during the mentor sessions to clear up any ambiguity. The Program Managers were proactive and ensured that timely support was given whenever needed.
31 Jan 2024
Batch of March 2022 | Principal Engineer / Manager at Qualcomm | United States
The program was top-notch, with excellent teaching, content, homework, and collaboration with other students. The support from the staff was outstanding, and I gained practical knowledge in using software and standard libraries for data analysis. This knowledge is already helping me and my team identify and implement new work initiatives.
07 May 2024
Batch of March 2023 | Data Science Enablement - Machine Learning COE - Asset / Product Owner at The Hartford Financial Services | United States
I gained knowledge, skills and experience that is helping with my career. The additional skills I gained are helping me with problem-solving and have increased my confidence. My program manager was very timely with information and reminders that helped me to stay on top. Thanks to MIT and Great Learning for such a great program!
07 May 2024
Batch of July 2022 | Advisor at Government Entity | Saudi Arabia
I am thankful to my mentors for making learning fun, interactive, and enjoyable. My Program Manager was there every step of the journey supporting, enabling, and easing any hurdles. Thank you, Great Learning, for this unforgettable experience.
26 Oct 2023
Batch of July 2022 | Colombia
I am very grateful for all the knowledge I acquired during this journey. My Program Manager was always available whenever I needed him. He always came up with solutions for all my issues and most importantly, he was always patient with me. The amazing team at Great Learning made everything easier. Thank you once again for such a great experience.
11 May 2023
Batch of January 2022 | Germany
The program gave me the guidance required to study data science and has pushed me to continuously work hard on my analytical and coding skills. Working on real business cases with leading experts in their fields has helped a lot to put the learned material into practice. I fully recommend this program for everyone who is committed to investing their time and learning more about data science.
11 May 2023
Batch of March 2022 | Taiwan
This program was one of the most career-boosting, enjoyable yet intense academic experiences for me. The program work was taught in Python and we have worked with a variety of real-world data and libraries, including pandas, matplotlib, seaborn, sklearn, surprise, numpy, and nltk. This program has the ability to teach you machine learning models, feature engineering, and quantifying business impacts - all of these within a span of 4 months. It also excels through its curriculum, mentoring sessions, and career guidance.
11 May 2023
Batch of March 2022 | France
The program played a crucial role in hastening my shift from an analytics position to a data science role by enhancing my understanding and proficiency in statistics and machine learning. The live classes conducted by MIT professors were extremely helpful, as they provided concrete python projects that allowed me to apply the concepts we learned every week. The program's speed was quite demanding, but it was also quite stimulating, and the guidance provided by data scientists and machine learning experts was extremely valuable.
11 May 2023
Batch of March 2022 | Saudi Arabia
The rigorous curriculum of the program provided me with the necessary abilities as a data scientist and led to my present diverse and highly successful commercial operations. The program gives a thorough understanding of the power of the data science profession. The online platform also allows students to adapt their studies around their own schedules.
11 May 2023
Batch of April 2023 | Switzerland
Initially, I faced difficulties in coding with Python. However, once I discovered the program's support system, I could easily ask any data science-related questions. As a result, I was able to comprehend the material well, particularly the coding aspect. Although the program is concise and rigorous, if you avail the opportunities available, you will not only enjoy the program but also gain immense confidence in handling any data science task. As a three-time Master's degree holder in Mechanical Engineering from Germany, I highly recommend attending this program.
18 Mar 2023
Batch of October 2023 | Senior Manager Of Operations at Grifols | United States
The best review of material. Answered every question and reviewed all the material. Very clear delivery and explanation of concepts with practical experience injected.
03 Jan 2023
Batch of November 2022 | United States
The prof. Jegelka is extremely intelligent, I appreciate classes with her, Thank you!
10 Jan 2023
Batch of September 2022 | United States
Thank you so much to the GL team and MIT professors for all your support in these past 3 months. I learned so much and I feel reborn as a new person.
Read all reviews

Program Fees

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

3,900 USD

  • Live Virtual Sessions from MIT Faculty
  • High-quality Content from MIT Faculty
  • Live Mentorship from Data Science and AI experts
  • 6 Hands-on Projects and 3-Week Capstone Project
  • 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
Apply Now
Apply Now

Candidates can pay the course fee through Credit/Debit Cards and Bank Transfer. For further details, please get in touch with the Great Learning team.

Contact Us

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: 4th Jul 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

20th Jul 2024

Frequently Asked Questions

Program Details
Is the program completely virtual?

Yes, the program has been designed keeping in mind the needs of working professionals. Thus, you can learn the practical applications of data science from the convenience of your home and within an efficient 12-week duration.

Is it necessary to bring my own laptop?


The learners are required to bring their own laptops; however, the necessary technology requirements shall be shared during the enrollment process.

How will my performance in the program be assessed?


The program has a broad scope, is challenging, and uses a continuous evaluation system. In order to evaluate a learner’s progress throughout the program, quizzes, case studies, assignments, and project reports are used.

What is the duration of this Applied Data Science certificate program?


The duration of this program is 12 weeks, which includes recorded lectures from award-winning MIT faculty. 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 Applied Data Science capstone project.

Will I receive a transcript or grade sheet after completion of the program?

No, Applied Data Science Program is an online professional certificate program offered by MIT Professional Education in collaboration with Great Learning. Since it is not a degree/full-time program offered by the university, therefore, there are no grade sheets or transcripts for this program. You will receive marks on each assessment to test your understanding and marks on each module to determine your eligibility for the certificate.

 

Upon successful completion of the program, i.e., after completing all the modules as per the eligibility of the certificate, you are issued a certificate from MIT Professional Education.

What certificate will I receive after completing the Applied Data Science Program from MIT Professional Education?


Upon successfully completing this program, learners will secure a professional certificate in Applied Data Science from MIT Professional Education.

What will happen if I can’t make it to a live session?


These live sessions will be recorded and posted on the LMS (Learning Management System) so that learners who couldn’t make it to a session or wish to attend it later can do so by watching the uploaded recordings.

Who will teach this Applied Data Science Program?


This program is taught by renowned MIT faculty who possess several years of experience and come highly recommended. Along with the teaching staff, the course also has highly qualified industry mentors who will direct you through live, personalized mentoring sessions as you work on hands-on projects.

What languages and tools will I learn in this program?


During this program, learners will gain proficiency in the most in-demand programming languages and tools, including Python, NumPy, Keras, TensorFlow, Matplotlib, and Scikit-Learn, among others.

What is unique about this Applied Data Science course syllabus?

This course syllabus is designed by considering the following aspects:

 

  • Renowned MIT faculty carefully crafted the curriculum to provide learners with industry-relevant tools and techniques and apply them to real-world problems.

  • The curriculum of this course covers essential Data Science techniques to deal with complex problems and prepare data-driven decision-makers for the future.

  • Learners will explore critical concepts of Data Analysis and Data Visualization, Machine Learning, Deep Learning, and Neural Networks.

  • The theory behind recommendation systems and their application to various sectors are also covered in the course material.

What is the program structure?

The MIT Applied Data Science Program lasts 12 weeks and is structured as follows: 

  • 2 Weeks: Foundational courses on data science with Python and Statistical Science
  • 6 Weeks: A core curriculum that includes hands-on applications and problem-solving, involving 58 hours of live virtual sessions by MIT faculty and industry experts
  • 1 Week: Project submissions
  • 3 Weeks: Final, integrative MIT Professional Education Applied Data Science capstone project

 

Note: The live virtual classes with MIT professors will occur on Mondays, Wednesdays, and Fridays at 9:30 AM EST.

What are the benefits of choosing this Applied Data Science course from MIT Professional Education?

This course is an excellent choice for those seeking knowledge and skills in Applied Data Science. The benefits of choosing this course from MIT Professional Education are as follows: 

  • Learn from distinguished MIT faculty through live online classes in the comfort of your home.
  • Boost your career transition with 1-on-1 career counseling, a review of your resume and LinkedIn profile, and an online portfolio that includes six hands-on projects and a 3-week capstone project.
  • Earn a Certificate of Completion from MIT Professional Education.
  • Take advantage of live mentorship from industry professionals on the application of faculty members' concepts.
  • Earn 3.0 Continuing Education Units (CEUs) on successful program completion.
What is the ranking of the Massachusetts Institute of Technology (MIT)?

MIT is ranked #1 university globally by QS World University Rankings 2023 and #2 in the best global universities in the U.S. News & World Report 2022-2023.

What is the Applied Data Science Program offered by MIT Professional Education?

The MIT Professional Education Applied Data Science Program is an all-encompassing course tailored to meet the learning needs of professionals seeking to advance their careers, tackle complex problems with innovative solutions, and contribute to a better future.

 

The program combines state-of-the-art online technology with traditional classroom instruction, fostering participation and teamwork and improving learning outcomes. Over 12 weeks, participants can enhance their data analytics skills by profoundly understanding the theories and practical applications of cutting-edge techniques, including supervised and unsupervised learning, regression, time-series analysis, neural networks, recommendation engines, and computer vision.

What is the required weekly time commitment?

For 5 weeks of MIT Faculty live lectures, each week involves:

 

  • 6 hours of live virtual sessions by MIT Faculty (Monday, Wednesday, and Friday)

  • 4 hours of mentored learning sessions (2 sessions every weekend)

  • 5 to 8 hours of self-study and practice (based on your background)

 

This amounts to an average time commitment of 15-18 hours per week.

 

For the remaining 7 weeks, an average time commitment of 12-16 hours per week is expected from the learners, which includes foundation/conceptual sessions, mentor learning sessions, capstone project work, self-study, and practice.

What will the timing of the live virtual sessions be?

The live virtual sessions with MIT faculty will be held on Mondays, Wednesdays, and Fridays at 9:30 AM EST. The mentorship sessions with industry experts will be held in small groups of learners on weekends. The exact timings will be determined based on the time zones of the learners in a particular mentorship group.

Why should I choose this Applied Data Science Program from MIT Professional Education?

MIT Professional Education is a distinguished platform that provides specialized and advanced applied data science programs, offering access to MIT's world-renowned research, knowledge, and expertise to working professionals in the fields of science and technology. As a critical component of MIT's vision, MIT Professional Education fulfills the mission of connecting practitioner-oriented education with industry and integrating industry feedback and knowledge into MIT's education and research.

Eligibility Criteria
Are there any prerequisites for this Applied Data Science Program from MIT Professional Education?


You should possess a working knowledge of computer programming and statistics.

What if I do not have the required programming and statistics experience?

The prerequisites of the program include working knowledge of programming and statistics. Suppose you do not possess either (or both) of them. In that case, you will have to put in extra effort to learn them before the program's commencement in order to cope with the curriculum designed by MIT Professional Education.

 

We, from Great Learning, will provide you with content that can be useful in understanding the fundamentals of programming (Python) and statistics. However, you would be required to put in extra effort and hours to complete the programming assignments.

Application Process
What is the deadline to enroll in this Applied Data Science Program?


The applications go through a rolling process that closes when the required number of seats in the cohort is filled. Please submit your application as soon as possible to boost your chances of getting a seat.

What is the application process to pursue this online Applied Data Science Program from MIT Professional Education?

Candidates must fulfill the eligibility requirements listed above to enroll in this course. The following is the typical application procedure for those candidates who qualify:

 

  • Step-1: Application Form

Candidates must fill out their online application form.
 

  • Step-2: Application Screening

Upon receiving the application, the program team will review it to determine your fit with the program.
 

  • Step-3: Program Enrollment

If chosen, candidates will be given an offer for the upcoming cohort. By paying the fee, they can reserve their seats.

Alumni Benefit
What are the other benefits that candidates acquire upon taking up this program?

Upon the successful completion of this program, learners become a part of MIT Professional Education's alumni community group and can access alumni benefits, that include a 15% discount towards any short programs offered by MIT Professional Education.

Why Applied Data Science
Are Data Science and Applied Data Science the same?

No, Data Science and Applied Data Science are different.

 

Data Science is a broad field that involves techniques and processes for gathering and analyzing data to generate insights, predictions, and strategies. It includes topics such as machine learning, artificial intelligence, and statistics.

 

Applied Data Science is the practice of using Data Science principles in different areas, such as e-commerce, healthcare, finance, and marketing. It focuses on utilizing data-driven approaches to design, develops, and deploy solutions to complex business problems. It focuses on the practical application of Data Science principles to derive insights and add value to different sectors of the economy.

What is the demand for Applied data scientists?

 

The demand for Applied Data Scientists has seen massive growth over the past few years and is most likely to increase the graph in the upcoming years. Glassdoor’s research says that the Data Scientist role is the #3 job in the United States in 2022. According to a study by the U.S. Bureau of Labor Statistics, the demand for Data Scientists is expected to rise 36% by 2031, which is much quicker than the average for all the other occupations. Data Scientists are one of the fastest-growing jobs in the world.

 

Is Applied Data Science worth it?

Yes, Applied Data Science is absolutely worth it! Applied Data Science involves the application of Data Science principles and practices to solve real-world problems. With Applied Data Science, you can use data to inform business decision-making, optimize complex systems, and make products and services more effective. 

 

Applied Data Science is an essential skill that can help you stand out in the job market and give you the knowledge and skills to help your organization stay ahead of the competition. It can open the door to more job opportunities, more efficient systems, and better decision-making.

What are the various applications of Data Science?

Numerous trending applications in the industry use Data Science. Some of the essential Data Science applications include:

 

  • Healthcare Services: Data Science can be used in Medical Image Analysis like tumor detection, etc., using a Machine Learning Method, Support Vector Machine (SVM).

     
  • Banking and Finance Sectors: Data Science can be used for fraud detection, risk modeling, customer data management, real-time predictive analytics, etc.

     
  • Transport: Data Science is used in several cars, like optimizing vehicle performance, fuel consumption patterns, etc. It can also be used in self-driving cars for vehicle monitoring. For example, Uber uses Data Science and Machine Learning to predict the weather, availability of customers and transportation, etc.

     
  • Manufacturing Industries: Data Science plays a vital role in the manufacturing industries, such as optimizing production, reducing costs, increasing profits, etc.

     
  • E-commerce: Data Science can be used to identify customer base, predictive analytics for estimating goods and services, identify the latest trends of each product, optimize pricing of the products for customers, and many more.

     
  • Image and Facial Recognition: Using Data Science and Machine Learning, you can identify a person in an image using a facial recognition algorithm. For example, when you upload a photo with your friends on Facebook, you get suggestions for tagging your friends in your picture. This automatic tag suggestion is an example of Image and Facial Recognition.

     
  • Airline Sectors: With the help of Data Science, airline sectors can now predict flight delays, they can choose which class of airplanes they can buy to suit their specific needs, plan airline routes whether to take a halt in any place or put out a direct flight and many more.

     
  • Gaming Sectors: In games, computers (opponents) collect data from your previous games and improve themselves in the upcoming games. For example, Chess.

 

There are several other industries that use Data Science for their applications.

What is applied data science?


Applied Data Science is a high/deep technical knowledge of how Data Science and its methodologies work. Applied Data Science involves modelling complicated problems, discovering insights, building highly advanced and high-risk algorithms, identifying opportunities through statistical and machine learning models, and visualization techniques for improving operational efficiency.

How do you become an applied data scientist?

You can become an Applied Data Scientist by:

 

  • Earning a bachelor’s degree in computer science, IT, mathematics/statistics, or any other Data Science related fields

  • Gaining professional experience in Data Science by working at any organization

  • Enrolling in an Applied Data Science Program from top universities, such as MIT, UC Berkeley, etc.

How much Salary can an applied data scientist earn?


According to the research by Glassdoor, the average salary earned by an Applied Data Scientist in the United States is $125,784 per annum. The pay scale ranges from $83K per annum to $194K per annum.

Fee and Payment
Can my employer cover the program fee?

 

We welcome corporate sponsorships and can help you through the process. 

 

[For more information, please write to us at adsp.mit@mygreatlearning.com or +1 617 468 7899]

Are there any extra costs associated with buying books, virtual learning resources, or license fees?


No. Through the Learning Management System (LMS), learners can access all the necessary learning materials online. There will be a list of recommended books and other resources for your in-depth reading pleasure because these fields are broad and constantly changing, so there is always more you can learn.

What is the course fee to pursue this Applied Data Science Program?


This professional course costs USD 3900, which candidates can pay through Credit/Debit Cards and Bank transfers. For further details, please get in touch with the Great Learning team.

What are my payment options?

 

Candidates can pay the course fee through Bank Transfer and Credit/Debit Cards. They can also avail PayPal payment options.


For further details, please get in touch with us at adsp.mit@mygreatlearning.com.

What is the refund policy for this program?

Please note that submitting the registration fee does constitute enrolling in the program, and the below cancellation penalties will be applied. If you are unable to attend your program, please review our dropout and refund policies below:

 

  • Dropout requests received within 7 days of enrollment and more than 42 days prior to the commencement of the program will incur no fee. Any payment received will be refunded in full.

  • Dropout requests received more than 42 days prior to the program but more than 7 days after the acceptance are subject to a cancellation fee of USD 250.

  • Dropout requests received 22-41 days prior to the commencement of the program are subject to a cancellation fee equal to 50% of the program fee.

  • Any dropout requests received fewer than 22 days prior to the commencement of the program are subject to a cancellation fee equal to 100% of the program fee.

  • No refund will be made to those who do not engage in the program or leave before completing a program for which they have been registered.

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 4th Jul 2024

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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.

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