Applied Data Science Program

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

  • 12 Weeks
  • Live Virtual Sessions by MIT Faculty
  • Mentorship by Experts

MIT Professional Education's Applied Data Science Program, with curriculum developed and taught by MIT faculty, is delivered in collaboration with Great Learning.

Why Join the Applied Data Science Program

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

QS World

World #1

MIT Rank in World Universities

QS World University Rankings, 2021

us news

U.S #2

MIT Rank in National Universities

U.S News & World Report Rankings, 2021

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

Languages and Tools covered

and more...

Hands-on Projects

Following a learn by doing pedagogy, the Applied Data Science Program 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.


Movie Lens Data Exploration

The GroupLens Research Project is a research group in the Department of Computer Science and Engineering at the University of Minnesota. In this project, we will perform exploratory data analysis to understand the popularity trends of movie genres and derive patterns in movie viewership.
Tools & Concepts: Python, SQL, Pandas, NumPy, Data Summary and Description
Learn more


Insurance Claim Prediction

A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. In this project, we will build a regression model to predict the cost of insurance claims using user information on age, gender, bmi, blood pressure, health conditions, as well as insurance claim details.
Tools & Concepts: Linear Regression, Model evaluation, Tuning, Exploratory Data Analysis, Python
Learn more


Network Congestion Type Prediction

The telecom industry is faced by a common challenge of network congestions due to various factors. To solve this problem, we will use data usage information of mobile phones from various telecom companies and find out a relation between the features of the mobile phone service provider (eg: bytes consumed through various services, etc.) and types of network congestion.
Tools & Concepts: Machine Learning, Classification, Model Tuning, Cross Validation
Learn more

Sales & Marketing

Forecasting Monthly Sales of French Champagne

Being able to make accurate predictions of future revenue can be hugely important for businesses. This project will focus on forecast the next monthly revenue of a french chamapagne brand, which will inform the decision-making process across all areas of the business, from purchasing decisions and marketing activity to staffing levels.
Tools & Concepts: Time Series Analysis, Predictive Modelling, Python for Time Series
Learn more


COVID-19 Global Forecasting

The capstone project is a focused approach to attempt a real-life challenge with the learnings from the program. In this project, you will use a combination of various datasets and models to forecast and predict daily cases and deaths. You will also create various plots to gain insights and showcase your results. the data has been collected from different sources like WHO, WorldoMeters, 1Point3Arces and many more to understand various aspects and build relevant features to use in models.
Tools & Concepts: Time Series Analysis, Python
Learn more


Product Recommendation System

Online E-commerce websites like Amazon use different recommendation models to provide different suggestions to different users. Amazon currently uses item-to-item collaborative filtering, which scales to massive data sets and produces high-quality recommendations in real-time. In this project, you will use similar concepts to create your own product recommendation system.
Tools & Concepts: Content-Based Recommendation Systems, Collaborative Filtering, Python
Learn more

Object Detection

Face Mask Segmentation

Predict and apply masks over the faces detected in images using Convoluted Neural Networks and image recognition algorithms. The goal of the project is to build a system that acts as a face detector to locate the position of a face in an image and apply a segmentation mask on the face. For this, we will utilise data from over 409 images and 1000 faces from the WIDER FACE dataset.
Tools & Concepts: Computer Vision, CNN, Transfer Learning, Object detection, Segmentation, TensorFlow
Learn more

News & Media

Sarcasm Detection

Past studies in Sarcasm Detection mostly make use of Twitter datasets collected using hashtag based supervision, but such datasets are noisy in terms of labels and language. Furthermore, many tweets are replies to other tweets and detecting sarcasm in these requires the availability of contextual tweets. In this hands-on project, the goal is to build a model to detect whether a sentence is sarcastic or not, using Bidirectional LSTMs. The dataset is collected from two news websites, and
Tools & Concepts: LSTM, Classification, GloVe, TensorFlow
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
Dr. Kumar Muthuraman
Devavrat Shah

Director, Statistics and Data Science Center (SDSC)


Dr. Kumar Muthuraman
Munther Dahleh

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


Dr. Kumar Muthuraman
Caroline Uhler

Henry L. & Grace Doherty Associate Professor, Institute for Data, Systems and Society (IDSS)


Dr. Kumar Muthuraman
John N. Tsitsiklis

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


Dr. Kumar Muthuraman
Stefanie Jegelka

Associate Professor, Dept. of Electrical Engineering & Computer Science and member of Computer Science and AI Lab (CSAIL) and Institute for Data,Systems and Society (IDSS)


Program Mentors
Bradford Tuckfield

Founder and Data Science Consultant

Kmbara (US)

Omar Attia

Senior Machine Learning Engineer

Apple (US)

Selcuk Baran

Research Science Manager

Amazon Web Services (US)

Matt Nickens

Manager, Partnership Science

Meta (US)

Fahad Akbar

Co-Founder & Core Contributor

PyCaret (Canada)

Udit Mehrotra

Senior Data Scientist

Dell Technologies (US)

Shannon Schlueter

Co-Founder, CTO and Data Scientist

Calido (US)

Tara Ann Thomas

Senior Analyst Data Scientist

Johnson & Johnson Vision (US)

Lee Tanenbaum

Global Director of Data Science and Analytics

Anheuser-Busch InBev (US)

Kalle Bylin

Data Engineer - Business Planning

IKEA (Sweden)

Vaibhav Verdhan

Analytics Leader, Global Advanced Analytics

AstraZeneca (UK)

Mustafa Shaikh

Senior Data Scientist

Walmart (Canada)

Andrew Marlatt

Data Scientist - Revenue Expansion

Shopify (US)

Marco De Virgilis

Senior Actuarial Data Scientist

Allstate (US)

Nikhar Shah

Senior Data Scientist

Nestlé (US)

Rohit Dixit

Senior Data Scientist

Siemens Healthineers (US)

Nitin Ranjan Sharma

Data Scientist

Novartis (India)

Animesh Gupta

Data Scientist

WestJet (Canada)

Your Learning Experience

The Applied Data Science Program 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.


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


Personalised Mentorship and Support

  • Weekly online mentorship from Data Science 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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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

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)

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)

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)

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

Learner Testimonials


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

Tanya Johnson

Customer Engineering Manager at Google


The 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

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

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

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

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


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.

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


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

Sabina Sujecka

Software Expert UX Designer at Orange


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

Arman Seuylemezian

Research Scientist at Jet Propulsion Laboratory


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

Matthew Wolf

Postdoctoral Researcher at University of Guelph


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

Abhishek M.

Principal Data Scientist at Nielsen

Ratings & Reviews by learners

All reviews (17)
13 Feb 2022
Batch of October 2021 | Founder/Owner at JD REI Holdings LLC | United States
A very good program that was loaded with information and offered much more than expected. I really liked the assistance and would recommend all to go for this program if you want to build your career in Data Science.
11 Feb 2022
Batch of October 2021 | Associate Director at UBS Singapore | Singapore
This program gave me good insights into data science, MIT Prof's classes were very thought-provoking and structured to understand. Mentor sessions were great in terms of covering the key concepts, reference materials and project guidance. The facilitators not only cover the queries but also were adding insights. This program has been my stepping stone to a career in Data Science
27 Jan 2022
Batch of August 2020 | Senior Neuroinformatics Research Associate at Vanderbilt University | United States
The succinct explanation of concepts by the professors from MIT was simplified yet detail oriented with examples which helped for clarifications of concepts. The case studies and the projects covered the practical use of the concepts in analysing a multitude of domains covering the AI/ML landscape.

Program Fees

Applied Data Science Program

USD 3900

  • Live Virtual Sessions from MIT Faculty
  • High-quality Content from MIT Faculty
  • Live Mentorship from Data Science 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

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.

Application Process


Fill the Application Form

Register by completing the online application form.


Application Screening

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


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: 9th Feb 2023

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

Cohort Start Date

Great Learning-Online Center

Live Virtual

18th Mar 2023

Frequently Asked Questions

Program Details
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.

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.

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.

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

The Applied Data Science Program offered by the Massachusetts Institute of Technology (MIT) Professional Education is designed in a comprehensive manner, providing world-class learning opportunities for professionals looking to progress their careers, innovatively address complex problems, and build a better future.


Their innovative use of cutting-edge online technology and conventional classroom instruction enhances learning outcomes while encouraging participation and teamwork. Learners can strengthen their data analytics skills in this 12-week program by learning the theory and practical application of techniques, such as supervised and unsupervised learning, regression, time-series analysis, neural networks, recommendation engines, and computer vision, to mention a few.

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

Through specialized, advanced education programs, MIT Professional Education opens doors to world-renowned MIT research, knowledge, and expertise for working professionals involved in science and technology.


MIT Professional Education, a critical component of MIT's vision, carries out the mandate to link practitioner-oriented education with industry and to integrate industry feedback and knowledge into MIT education and research.


What is the ranking of the Massachusetts Institute of Technology (MIT)?

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

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 6 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 applications of concepts taught by faculty members.

  • Earn 3.0 Continuing Education Units (CEUs) on successful program completion.

What is the program structure?

The program lasts 12 weeks and is structured as follows:


  • 2 Weeks: Foundational courses on 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 Applied Data Science capstone project


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

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 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.

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 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.

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.

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 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.

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.

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.

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 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.

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.

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.

Fee and Payment
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

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.

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.

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.

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 or +1 617 468 7899]

Why Applied Data Science
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.

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.


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.

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.

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.

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 or +1 617 468 7899

Application Closes 9th Feb 2023

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Delivered in Collaboration with:

MIT Professional Education's Applied Data Science Program, 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.

Introduction to the Applied Data Science Program from MIT Professional Education


Globally, a variety of professional Applied Data Science courses are accessible. Yet, there are a number of reasons for working professionals to pursue this Applied Data Science professional certificate program from MIT Professional Education in collaboration with Great Learning. The reasons are listed below:


  • MIT (Massachusetts Institute of Technology) is the world's highest-ranked university.

  • As per the rankings by QS World University Rankings 2023, MIT has ranked #1 university globally and ranked #2 worldwide as per the U.S. News & World Report 2022-2023.

  • For working professionals in the science and technology fields, MIT Professional Education opens doors to world-renowned MIT research, knowledge, and expertise through specialized, advanced education programs.

  • A crucial element of MIT's vision, MIT Professional Education carries out the directive to connect practitioner-oriented education with industry and to incorporate industry feedback and knowledge into MIT education and research.

Benefits of Pursuing MIT Professional Education Applied Data Science Certificate Course

Choosing this MIT Professional Education course has the following benefits:


  • Pursue the MIT Professional Education Applied Data Science certificate course and learn this cutting-edge technology from award-winning MIT faculty and mentors.

  • These distinguished MIT faculty members have created the curriculum to develop skills that are in high demand in the workplace.

  • Learners can demonstrate their Applied Data Science Leadership by creating a portfolio of 6 hands-on projects (2 of which are mandatory) and a 3-week capstone project on a real-world business problem.

  • Learners will work in a robust collaborative environment to interact with peers in Applied Data Science.

  • Enhance your career transition by receiving one-on-one career counseling and having your resume and LinkedIn profile reviewed. 

  • Apply concepts taught by the faculty by participating in live mentoring sessions and receiving advice from Applied Data Science experts.

  • Upon successful program completion, receive 3.0 Continuing Education Units (CEUs).


Details about MIT Professional Education Applied Data Science Course

In this comprehensive MIT Professional Education Applied Data Science online course, the participants will acquire all the critical skills necessary to master Applied Data Science and Machine Learning. Let’s go through the extensive details about the program:


Learnings Outcomes of the Course

The learning outcomes of this course are drafted below:

  • Explore the intricacies of Applied Data Science tools and techniques and their significance to real-world problems.

  • Build solid foundations in Python, Mathematics, and Statistics for Applied Data Science.

  • Use a range of Machine Learning techniques to tackle challenging issues and make data-driven business decisions.

  • Gain familiarity with two notable realms of Machine Learning, Deep Learning & Neural Networks, and discover how to utilize these techniques in areas like Computer Vision.

  • Become familiar with the theory underlying recommendation systems, then consider how it might be applied to various business contexts and industries.

  • Discover how to put together a portfolio of projects suitable for the industry to showcase your expertise in extracting business insights from data.


Applied Data Science Syllabus

The list of the topics covered in the curriculum is drafted below:

  • The course commences with the foundations of Data Science, including:

    • Python - NumPy, Pandas, Arrays, Exploratory Data Analysis (EDA), and Data Visualization)

    • Statistics - Descriptive Statistics, Probability Distributions, Bayes’ Theorem, and Inferential Statistics

  • Followed by the Data Science foundations, learners will be introduced to Data Analysis and Visualization.

  • Moving forward, learners will explore several Machine Learning techniques, including Supervised and Unsupervised Learning, Regression, Classification, Model Evaluation, Decision Trees, Random Forests, and Time Series.

  • Lastly, learners will become familiar with Deep Learning and Recommendation Systems.


[Explore Applied Data Science Syllabus]


Course Eligibility

The following are the eligibility criteria for this course:

  • Working professionals interested in a career in Applied Data Science and Machine Learning

  • Working professionals eager to implement Data Science and Machine Learning initiatives within their organizations

  • Entrepreneurs who want to innovate with the aid of Applied Data Science and Machine Learning techniques


Secure a Professional Certificate in Applied Data Science from MIT Professional Education

Upon successfully completing this course, learners will secure a professional certificate in Applied Data Science from MIT Professional Education. Later, learners will be eligible to apply for a variety of jobs in the field of Applied Data Science, which is discussed in the next section.


Applied Data Science Jobs

Top Applied Data Science jobs are highly competitive and require a combination of technical and business skills. Companies are increasingly looking for candidates with strong technical abilities and creative problem-solving skills. The job market will become increasingly competitive as demand for Applied Data Scientists continues to grow. It is vital to prepare for the challenge by pursuing the necessary training and education.


A few of the job openings are listed below:


The jobs mentioned above are just for reference. Numerous job openings are accessible across the world. Upon successful completion of the program, learners will be eligible to apply for several positions, including:


  • Applied Data Scientist

  • Data Scientist

  • Data Analyst

  • Applied Data Analyst

  • Applied Machine Learning Scientist

  • Applied Statistics Engineer, and many more


Whether you are just starting out in the field of Applied Data Science or you are a seasoned professional, the opportunities available to you in this growing field are vast and exciting. With the proper education and experience, you can secure a role in this high-paying and rapidly evolving industry.


Applied Data Science Salary

An Applied Data Scientist's salary can vary depending on the individual's experience and credentials, as well as the country and industry. Generally, however, they can expect to earn anywhere in the range of US$80,000 to US$160,000 per year. This range also takes into account any bonuses and perks associated with the job, such as stock options and other forms of compensation.


At the entry level, an Applied Data Scientist can expect to earn an annual salary between US$50,000 and US$70,000. This salary range covers a wide range of roles and responsibilities, including working with external data sources, creating data visualizations, building predictive models, and working with Machine Learning algorithms.


For more experienced and senior-level positions, an Applied Data Scientist's salary can range from US$100,000 to US$160,000 per year. These roles usually require at least a few years of experience. Usually, they involve higher-level responsibilities like leading teams of Applied Data Scientists and Machine Learning Engineers, building complex data pipelines, and managing data-driven projects.