Applied Data Science Program

Item: Applied Data Science Program
Rating: 5
Author: Sreekanth Natrajan

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.

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

Live Virtual Sessions from world-renowned MIT Faculty

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

Certificate of Completion from MIT Professional Education

World #1

MIT Rank in World Universities

QS World University Rankings, 2021

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.

Curriculum

The Applied Data Science Program curriculum has been carefully crafted by MIT faculty to provide you with the skills, knowledge, and confidence you need to flourish in the industry. By encompassing the most business-relevant technologies, such as Machine Learning, Deep Learning, Recommendation Systems, ChatGPT, Generative AI and more, it prepares you to be an important part of data science efforts at any organization.

Curriculum designed by MIT faculty in Data Science and Machine Learning
Become a Data Science decision maker by learning Deep Learning, Machine Learning, Recommendation Systems, and more.
Taught in Python

Download Curriculum

Weeks 1-2

In the first two weeks, we will cover the foundational concepts for data science that form the building blocks of the course and help you prepare to be successful in the program.

Module 1: Foundations for Data Science

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

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, MDS 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

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

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

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.

Graph Neural Networks

These neural networks directly operate on graph structures. You will learn about the fundamentals of Graph Neural Networks (GNN).

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

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: Milestone 2

In week 11, you will work toward completing 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 3.0 Continuing Education Units (CEUs)

Earn a professional certificate in Applied Data Science from 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 across the world.

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 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.

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 adsp.mit@mygreatlearning.com 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.

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.

Ratings & Reviews by learners

All reviews (17)

Joseph DelVecchio

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.

Sreekanth Natrajan

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

Chenchal Subraveti

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.

Read all reviews

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

Contact Us: +1 617 468 7899

Why Join the Applied Data Science Program

Live Virtual Teaching by MIT Faculty

Personalized Mentorship and Support

Practical, Hands-on Training

Live Virtual Sessions from world-renowned MIT Faculty

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