mit big data science (coursework)

Data Science and Big Data Analytics: Making Data-Driven Decisions

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Could you be using data more effectively? Uncover your data’s true value and learn how to leverage it with the latest and most powerful tools, techniques, and theories in data science from industry experts and renowned MIT faculty.  View the week to week course schedule here.

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By: MIT Open Learning on October 27th, 2021 12 Minute Read

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10 Resources for Learning Data Science Online from MIT Open Learning

It's no surprise that data science savvy professionals are in high demand in today's job market. With a 650% increase in data science jobs since 2012 , now is the time to familiarize yourself with data science and other key topics in computer science.

Learning data science online doesn't have to be expensive or intimidating. MIT Open Learning offers a number of online data science resources that range in cost and time commitment, including courses and programs from OpenCourseWare, MITx Refugee Action Hub (ReACT), and MIT xPRO. To better understand the benefits of these different resources, scroll to the comparison chart at the end of this post.

Whether you need to brush up on basics, take a deep dive, or gain a credential that can be used to further your academic or professional goals, MIT Open Learning has an online data science course for you! 

Get started by exploring the online data science learning resources below:

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• Data Science and Machine Learning: Making Data-Driven Decisions

Advance your Data Science Skills to solve business problems with this online program for professionals.

12 Weeks | Learn from MIT Faculty | Mentorship from Industry Experts

In collaboration with:

Why Join the Data Science and Machine Learning Program

Learn from renowned mit faculty.

• Build industry-valued skills at your own pace with recorded video lectures
• Curriculum designed by MIT leaders in computing and statistics

Personalized mentorship and support

• Live mentorship and guidance from data science and machine learning practitioners on weekends
• Collaborative yet personalized sessions in small groups

Practical, hands-on training

• Work on 3 industry-relevant projects and 50+ case studies
• Graded activities, assessments, and discussions on Great Learning forums

>>> View all program benefits and apply through Great Learning

The Data Science and Machine Learning: Making Data-Driven Decisions program has a curriculum carefully crafted by MIT faculty to provide you with the skills and knowledge to apply data science techniques to help you make data-driven decisions. This data science professional certificate program has been designed for the needs of data professionals looking to grow their careers and enhance their data science skills to solve complex business problems. In a relatively short period, the program aims to build your understanding of most industry-relevant technologies today, such as machine learning to deep learning, network analytics, recommendation systems, graph neural networks, time series, ChatGPT and Generative AI. Hence, the program is best suited for learners with prior exposure to working with data using some tools and applying basic algorithms and methods.

Upon completing the program, you will be awarded a Certificate of Completion from MIT IDSS and 8 Continuing Education Units (CEUs). CEUs are a nationally recognized method of quantifying the time spent learning during professional development and training activities.

>>> See the full curriculum and apply through Great Learning

Program Faculty

Testimonials

“Sincerely, I have taken several programs, but the experience from this program is simply different from multi-dimensional perspectives. I would recommend this program again and again to professionals who would like to upgrade their skills in Data Science and Machine Learning.” Oluwarotimi Williams Samuel Research Scientist, Shenzhen Institute of Advanced Technology

>>> Read more testimonials on the Great Learning website

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Data Science and Big Data Analytics: Making Data-Driven Decisions

Monday, October 23, 2017

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About this Event

MIT xPRO Online Professional Development Course

Course starts: October 23, 2017

Enroll today: http://bit.ly/2v5OaIc

Course Description

Every day, your organization generates new data on your customers, your processes, and your industry. But could you be using this data more effectively? Discover how to turn big data into even bigger results in this eight-week online course and earn an MIT Certificate on Data Science as well as 1.8 Continuing Education Units (CEUs) upon completion.

This course was developed by over ten MIT faculty members at the Institute for Data, Systems and Society (IDSS). It is specially designed for data scientists, business analysts, engineers, and technical managers looking to learn the latest theories and strategies to harness data.

Not sure this course is for you? Download this sample case study on building a movie recommendation system taken from the course so you can get a sneak peek of what’s included in the course.

Turn Your Knowledge into Action

Through digital lectures and hands-on case studies based on examples from real-world business scenarios, you’ll acquire the theory, strategies, and tools you need to:

• Apply data science techniques to your organization’s data management challenges.
• Identify and avoid common pitfalls in big data analytics.
• Deploy machine learning algorithms to mine your data.
• Interpret analytical models to make better business decisions.
• Understand the challenges associated with scaling big data algorithms.
• Convert datasets to models through predictive analytics.

Real-World Case Studies & Hands-on Projects

Ever wondered how top companies perfect their recommendation systems? Or how auto manufacturers develop their GPS technology? In Data Science and Big Data Analytics: Making Data-Driven Decisions, you’ll be able to examine over 20 case studies and apply your knowledge by:

• Tracking the 2D and 3D position of objects with a Kalman filter.
• Building your own movie, music, and product recommendation systems, just like Netflix or Pandora.
• Automatically clustering news stories with a spectral technique algorithm.
• Predicting wages with a linear regression model.
• Exploring one or two layer perceptrons to assess their decision boundaries.
• Using network-theoretic ideas to identify new candidate genes that might cause autism.

What You'll Learn

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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..

• Live Virtual Sessions by MIT Faculty
• Mentorship by Experts

Contact Us: +1 617 468 7899

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.

Personalized Mentorship and Support

• Live mentorship and guidance from data science practitioners on weekends
• Collaborative yet personalised sessions in small groups

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

Personalised mentorship and guidance from data science practitioners

Hands-on training via 2 projects and 1 capstone, 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 Rank in World Universities

QS World University Rankings, 2023

MIT Rank in National Universities

U.S News & World Report Rankings, 2022

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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

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.​​​​​​​

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.

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.

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
• 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

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.

Languages and Tools covered

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 - Python and Statistics, 1 Project of their choice out of the 5 projects associated with core courses taught by MIT Faculty, and a 3-week capstone project. Below are samples of potential project topics.

Capstone - Marketing Campaign Customer Segmentation

Capstone - loan default prediction, capstone - malaria detection, capstone - facial emotion detection - dl cnn.

Entertainment

Capstone - Music Recommendation Systems

Transportation

Capstone - Used Card Price Prediction

Amazon ai product recommendation system, diabetes analysis, malaria detection, predicting potential customers, 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 Shah

Professor, EECS and IDSS, MIT

Munther Dahleh

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

Caroline Uhler

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

John N. Tsitsiklis

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

Stefanie Jegelka

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

Program Mentors

Fahad Akbar

Senior Manager Data Science

Bain & Company

Udit Mehrotra

Data Science Specialist

McKinsey & Company

Shannon Schlueter

Director of Data Science

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

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)

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

Jamal Madni

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

The adeptness, simplification and succinct explanation of concepts by the MIT professors was simplified yet detail oriented with examples and simple numerical illustrations. I continue to watch / refer to the recorded video lectures for clarifications of concepts.

Chenchal Subraveti

Sr. Research Analyst, Vanderbilt University (USA)

Learner Testimonials

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

Tanya Johnson

Customer Engineering Manager at Google

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

Adrian Mendoza

Director, UX Strategy & Design at Deloitte

The 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

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

Khashayar Ebrahimi

Senior Engineer - Solver Developer at Gamma Technologies

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

Chad Barrett

Insights Analyst at Equinix

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

Wasyl Baluta

CEO/CTO at Plexina Inc.

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

Pradeep Podila

Health Scientist- Senior Service Fellow at CDC

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

Kalpana Vetcha

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

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

Sabina Sujecka

Software Expert UX Designer at Orange

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

Arman Seuylemezian

Research Scientist at Jet Propulsion Laboratory

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

Matthew Wolf

Postdoctoral Researcher at University of Guelph

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

Abhishek M.

Principal Data Scientist at Nielsen

Program Fees

• 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

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: 5 th Sep 2024

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

Live virtual.

21 st Sep 2024

Frequently Asked Questions

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.

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

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.

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.

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.

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

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.

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.

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.

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.

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 .

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

[For more information, please write to us at [email protected] or +1 617 468 7899]

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.

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.

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 [email protected] .

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 [email protected] or +1 617 468 7899

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Check out the program and fee details in our brochure

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