Data Science and Machine Learning: Making Data-Driven Decisions

Advance your Data Science Skills to solve business problems with this hand-made program for professionals

12 Weeks
Learn from MIT Faculty
Mentorship from Industry Experts

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Why Join the Data Science and Machine Learning Program

Learn from renowned
MIT Faculty

Recorded Video Lectures from world-renowned MIT Faculty.
Curriculum designed to build industry-valued skills.

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.

Recorded lectures from 11 world-renowned MIT faculty and Instructors.

Personalized mentorship and guidance from data science & machine learning practitioners.

Work on 3 industry-relevant hands-on projects and 50+ case studies.

Curriculum covering Data Science, Machine Learning and more.

Who is this Program for?

Data scientists, data analysts, and professionals who wish to turn large volumes of data into actionable insights.

Early career professionals and senior managers, including Technical managers, Business intelligence analysts, IT practitioners, Management consultants, and Business managers.

Those with some academic/ professional training in applied mathematics/ statistics. Participants without this experience will have to put in extra work and will be provided support by Great Learning.

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Certificate of Completion from MIT IDSS

MIT IDSS Benefits:

Certificate from the MIT Schwarzman College of Computing and IDSS, upon successful completion of the program
Exclusive discounts on current and future IDSS online course offerings
Subscription to the IDSS newsletter
Advance notice regarding upcoming courses, programs, and events

World #1

MIT Rank in World Universities

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U.S #2

MIT Rank in National Universities

U.S News & World Report Rankings, 2022

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

Curriculum

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

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.

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Weeks 1-2: Foundations of Data Science

In the first two weeks, we will cover the foundational concepts for Data Science that form the building blocks of the course and will help you sail through the rest of the journey with ease.

Python for Data Science

1 Case Study

Python, for Data Scientists and Machine Learning specialists, is a lingua franca owing to the immense promise of this widely-used programming language. To strengthen your Python foundations, this module focuses on NumPy, Pandas, and Data Visualization.

Numpy
Numpy is a Python package for scientific computing that enables one to work with multi-dimensional arrays and matrices.
Pandas
Pandas is an open-source and powerful library in Python that is used to analyze and manipulate data.
Data Visualization
Data Visualization means dealing with the graphic representation of data, which effectively generates insights from data by using matplotlib, seaborn, etc., libraries.

Statistics for Data Science

1 Case Study

This week’s chapter will help you understand the role of statistics in helping organizations make effective decisions, learn its most widely-used tools, and learn to solve business problems using analysis, data interpretation, and experiments. It will cover the following topics:

Descriptive Statistics
It gives you the fundamental measures of a statistical summary of the data.
Inferential Statistics
It will explore the areas of distributions and parameter estimation, ultimately allowing you to make inferences from the data.

Week 3: Learning break

Week 4: Making Sense of Unstructured Data

In this week, you will learn how to apply different ML techniques to discover patterns and insights in unstructured data.

Introduction

Here, you will learn about one of the essential aspects of ML - Unsupervised Learning.

What is unsupervised learning, and why is it challenging?
Unsupervised learning algorithms will assist you in analyzing and clustering unlabelled data sets. This chapter teaches you about unsupervised learning and the challenges encountered using these algorithms.
Examples of unsupervised learning
This chapter will make you understand the implementation of several unsupervised learning algorithms with examples.

Clustering

2 Case Studies

Clustering is an unsupervised learning technique to group similar sets of data points. The next module of the course in Data Science from MIT will introduce you to the widely used clustering techniques, i.e., K-means clustering.

What is clustering?
Here, we will discuss the basic intuition behind clustering and why it is incredibly prevalent in numerous industries.
When to use clustering?
This chapter will teach you the procedure for using clustering techniques.
K-means preliminaries
This chapter will make you understand a few preliminaries before beginning with K-means clustering.
The K-means algorithm
The K-means algorithm in clustering is one of the most commonly implemented unsupervised learning algorithms for resolving clustering problems in Data Science or Machine Learning.
How to evaluate clustering?
This chapter will make you familiar with the procedure to evaluate clustering.
Beyond K-means: What makes a cluster?
Here, you will learn several techniques that make a cluster.
Beyond K-means: Other notions of distance
This chapter will familiarize you with several other types of distance methods in clustering and teach you the use cases for the same.
Beyond K-means: Data and pre-processing
Data pre-processing is a technique to clean raw data to use for machine learning applications. It is the initial and foremost step when implementing a machine learning project.
This chapter will discuss why pre-processing is necessary for Data Science and all the steps involved.
Beyond clustering
In this chapter, you will understand all the crucial topics beyond clustering and its applications.

Spectral Clustering, Components, and Embeddings

2 Case Studies

Spectral clustering is one of the most widely implemented techniques for cluster graphs and networks. Here, you will learn about spectral, modularity clustering, and the PCA algorithm. This module will discuss spectral clustering and its components and embeddings.

What if we do not have features to describe the data or not all are meaningful?
This chapter will teach you how to provide a solution if you do not have any features to describe the data or if not all are meaningful.
Finding the principal components in data and applications
Principal Component Analysis is a method to reduce the complexity of an unsupervised machine learning model. In Layman’s terms, Principal Component Analysis is like eliminating the input variables for a predictive model to avoid overfitting.
The magic of eigenvectors I
In this chapter, you will understand the procedure to implement eigenvectors in a matrix.
Clustering in graphs and networks
Here, you will gain an understanding of clustering in graphs and networks.
Features from graphs: The magic of eigenvectors II
Here, you will understand the procedure to implement eigenvectors in a matrix using several features from graphs.
Spectral clustering
Spectral clustering will enable you to reduce complex multi-dimensional datasets into identical data clusters in rarer dimensions.
Modularity Clustering
The measure of the strength of a network division into clusters is called Modularity clustering.
Embeddings: New features and their meaning
An embedding is a moderately low-dimensional space to translate high-dimensional vectors, which assists in making it easier to do machine learning on enormous inputs.

Week 5: Learning Break with Hands-on Masterclass 1

Week 6: Regression and Prediction

In this week, you will explore the classical and modern regression methods for prediction and inferential purposes.

Classical Linear and Nonlinear Regression and Extensions

2 Case Studies

Here, you will learn about linear and nonlinear regression together with their extensions, including the crucial case of logistic regression for binary classification and causal inference, where the goal is to understand the effects of actively manipulating a variable as opposed to passively measuring it.

Linear regression with one and several variables
Here, you will understand the procedure to implement linear regression with one and several variables.
Linear regression for prediction
This chapter will familiarize you with the procedure to implement linear regression for predictive analysis.
Linear regression for causal inference
This chapter will familiarize you with the procedure to implement linear regression for causal inference.
Logistic and other types of nonlinear regression
Logistic regression is a simple classification algorithm in Machine Learning that predicts the categorical dependent variables using independent variables.
This chapter will familiarize you with all the fundamentals of Logistic Regression and other types of nonlinear regression in Machine Learning.

Modern Regression with High-Dimensional Data

1 Case Study

In the next module of this Data Science for working professionals course, you will learn about modern regression with high-dimensional data or finding a needle in a haystack. For large datasets, it becomes necessary to sort out which variables are relevant for prediction and which are not. Recent years have witnessed the development of new statistical techniques, such as Lasso or Random Forests, that are computationally superior to large datasets and automatically select relevant data.

Making good predictions with high-dimensional data
This chapter will teach you the process of making good predictions with high-dimensional data.
Avoiding overfitting by validation and cross-validation
Overfitting occurs when a model over-trains the data. In Layman's terms, suppose a model learns the detail and noise within the training data. In that case, the training data will negatively affect the performance of the model on new data.
This chapter will teach you the process of avoiding overfitting through validation and cross-validation techniques.
Regularization by Lasso, Ridge, and their modification
Here, you will understand regularization by Lasso, Ridge, and their modification.
Regression Trees, Random Forest, Boosted Trees
Regression Trees are built using binary recursive partitioning, an iterative process that splits the data into partitions or branches. It later splits each portion into smaller groups as the process advances every branch.
Random Forest is a prevalent supervised Machine Learning algorithm that constitutes numerous decision trees on the given innumerable subsets of a dataset. Later, it will calculate the average to enhance the data set's predictive accuracy.Boosting is a meta-algorithm in Machine Learning, which transforms robust classifiers from several weak classifiers.
Boosting can be distinguished as Gradient boosting and Adaptive (ADA) boosting.

The Use of Modern Regression for Causal Inference

2 Case Studies

This part will cover regression and causal inference to explain why “correlation does not imply causation” and how we can overcome this intrinsic limitation of regression by resorting to randomized control studies or controlling for confounding.

Randomized Control Trials
This chapter will teach you the process of identifying and working with Randomized Control Trials.
Observational Studies with Confounding
Confounding is a common hazard of observational clinical research opposing randomized experiments. Yet, it can easily pass unrecognized, although its recognition is essential for significantly interpreting causal relationships, like evaluating treatment effects.

Week 7: Learning Break with Hands-on Masterclass 2

Week 8: Classification and Hypothesis Testing

In this week, you will learn about the basics of anomaly detection, classification, and fundamentals of hypothesis testing, which is the formalization of scientific inquiry. This delicate statistical setup obeys a specific set of rules that will be explained and put in context with classification.

Hypothesis Testing and Classification

1 Case Study

In this module of the MIT Data Science certificate program, you will learn Hypothesis testing and several classification algorithms. Hypothesis Testing is a technique to perform experiments using the observed/surveyed data. As the name indicates, classification is a technique to classify a data set into different categories and can be performed on both structured and unstructured data.

What are anomalies? What is fraud? Spams?
Anomalies occur when databases are inadequately planned and un-normalized, where all the data is stored in one table. Fraud, as the name suggests, is a fraudulent act with no authorization. Spam is unsolicited digital communication, such as sending messages, emails, etc., to vast amounts of people for commercial purposes.
In this chapter, you will understand the procedure to detect anomalies, fraud, and filter spam in Machine Learning.
Binary Classification: False Positive/Negative, Precision / Recall, F1-Score
Binary classification is a supervised machine learning technique, where the categories are predefined and classified into new probabilistic observations. When there are two categories, it is called binary classification.
Logistic and Probit regression: Statistical binary classification
Probit regression is a method where the dependent variable takes only two values. This chapter will discuss all the essential concepts, like Logistic regression, Probit regression, and Statistical binary classification.
Hypothesis testing: Ratio Test and Neyman-Pearson p-values: Confidence
Here, you will gain an understanding of all the critical concepts of hypothesis testing.
Support vector machine: Non-statistical classifier
Support Vector Machine, shortened to SVM, is another popular Machine Learning algorithm used for regression and classification problems.
Perceptron: Simple classifier with elegant interpretation
A perceptron is an artificial neuron, or plainly, a mathematical model of a biological neuron. This chapter will familiarize you with perceptron and its various concepts.

Week 9: Learning Break with Hands-on Masterclass 3

Week 10: Deep Learning

Deep learning has emerged as a driving force in the ongoing technological revolution. The essence of Deep Learning lies in its ability to imitate the human brain in processing data for various purposes, that too without any human supervision. Neural networks are at the heart of this technology. This week will take you beyond traditional ML and into the realm of Neural Networks and Deep Learning. You’ll learn how Deep Learning can be successfully applied to areas such as Computer Vision and more.

Deep Learning

1 Case Study

Here, the learners will understand all the critical concepts of Deep Learning, such as image classification, back-propagation, transfer learning, NLP, speech recognition, and much more.

What is image classification? Introduce ImageNet and show example
Image classification is a fundamental concept in deep learning. It identifies objects in an image by training a model through experimentation with labeled images.
This chapter will teach you the process of identifying objects in an image and introduce you to ImageNet, along with several examples.
Classification using a single linear threshold (perceptron)
Here, you will learn the process of implementing classification techniques using a single linear threshold (perceptron).
Hierarchical representations
Here, you will learn the process of representing deep learning models in a hierarchical structure.
Fitting parameters using back-propagation
In this chapter, you will learn how to find coefficients (parameters) for one or numerous models for fitting data.
Non-convex functions
This chapter will familiarize you with non-convex optimization functions in deep learning.
How interpretable are its features?
Here, you will understand how the features are interpretable.
Transfer learning
Transfer learning is a widely implemented deep learning approach. It is a model developed for an application that can be reused as the initial point for a model on a second application.
Other applications I: Speech recognition
Speech recognition is a technique to transform human speech into written text by recognizing the voice of an individual.
Other applications II: Image Classification
Image classification is a computer vision task that involves training algorithms to categorize images into predefined classes.

Week 11: Recommendation Systems

As organizations are increasingly leaning towards data-driven approaches, an understanding of recommendation systems can help not only data science experts but also professionals in other areas such as marketing who, too, are expected to be data literate today. Learn why recommendation systems are now everywhere and some insight on what is required to build a suitable recommendation system by covering statistical modeling and algorithms.

Recommendations and Ranking

1 Case Study

Recommendation System algorithms, simply put, suggest relevant items to users - explaining the trends of their usage across a range of industries and their central role in revenue generation.

What does a recommendation system do?
As the name indicates, recommendation systems assist you in predicting the future preference of any product and recommending the best-suited items to users.
In this chapter, you will understand the procedure to utilize a recommendation system to choose the best products for users.
So what is the recommendation prediction problem? And what data do we have?
The technique where the system predicts whether an individual or a business likes the product (a classification problem) or the reviews or ratings by them (a regression problem) is known as the recommendation prediction problem.
Using population averages
Here, you will understand the procedure for using population averages.
Using population comparisons and ranking
Here, you will understand the procedure for using population comparisons and ranking.

Collaborative Filtering

1 Case Study

Collaborative filtering is an aspect of recommendation systems with which we interact quite frequently. Upon collecting data on the preferences of multiple users, collaborative filtering makes predictions for the choice of a particular user.

Personalization using collaborative filtering using similar users
Here, you will understand the procedure to use collaborative filtering with the help of similar users.
Personalization using collaborative filtering using similar items
Here, you will understand the procedure to use collaborative filtering with the help of similar items.
Personalization using collaborative filtering using similar users and items
Here, you will understand the procedure to use collaborative filtering with the help of similar users and items.

Personalized Recommendations

1 Case Study

As suggested by the name itself, personalized recommendations work to filter out recommendations that are personally relevant for a user, based on their browsing trends, etc.

Personalization using comparisons, rankings, and user items
Here, you will learn how to utilize personalization recommendations with the help of comparisons, rankings, and user items.
Hidden Markov Model / Neural Nets, Bipartite graph, and graphical model
The Hidden Markov Model (HMM) is a statistical Markov model in which the system being modeled is regarded as a Markov process with hidden/unobserved states.
Using side information
This chapter will familiarize you with the procedure to use side information with the assistance of Meta-Prod2Vec.
Building a system: Algorithmic and system challenges
This chapter will familiarize you with the procedure to make a system considering algorithmic and system challenges.

Week 12: Networking and Graphical Models

In this week, you will get a systematic overview of methods for analyzing large networks, determining important structures in such networks, and inferring missing data in networks. An emphasis is placed on graphical models, both as a powerful way to model network processes and to facilitate efficient statistical computation.

Introduction

In this module of the MIT Data Science course, you will get to know what networks are and how we can represent networks with their practical use-cases around us.

Introduction to networks
You can define a network as a group of two or more computer systems linked together using several hardware components, such as hubs, switches, and more.
Examples of networks
In this chapter, you will gain an understanding of all the examples of networks.
Representation of networks
This chapter will familiarize you with the procedure to represent networks.

Networks

1 Case Study

In the next module of the MIT Data Science online course, you will learn about the standard descriptive measures of a network, such as centrality, closeness & betweenness, and standard stochastic models for networks, like Erdos-Renyi, preferential attachment, infection models, notions of influence, etc.

Centrality measures: degree, eigenvector, and page rank
This chapter will familiarize you with the procedure to implement centrality measures, such as degree, eigenvector, and page rank.
Closeness and betweenness centrality
Here, you will gain an understanding of closeness and betweenness centrality.
Degree distribution, clustering, and small world
Here, you will gain an understanding of Degree distribution, clustering, and the small world.
Network models: Erdos-Renyi, configuration model, preferential attachment
The Erdos-Renyi model assists you in creating random networks or graphs on social networking. The configuration model is a technique to generate random networks from a given degree sequence. Preferential attachment is a method in which new network members attempt to establish a connection with the more prevalent existing members.
Stochastic models on networks for the spread of viruses or ideas
Here, you will gain an understanding of stochastic models on networks for the spread of viruses or ideas.
Influence maximization
The problem of identifying a small subset of nodes (seed nodes) in a social network that may maximize the spread of influence is called influence maximization.

Graphical Models

1 Case Study

Here, you will get to know how to use graphical models to estimate and display a network of interactions.

Undirected graphical models
In this chapter, you will learn about undirected graphical models.
Ising and Gaussian models
Ising model specifies the joint probability distribution of a vector to understand phase transitions. A Gaussian model is a two-dimensional normal distribution of the concentration in the crosswind and vertical directions centered around the downwind axis from the initial point.
Learning graphical models from data
Here, you will gain an understanding of several graphical models from data.
Directed graphical models
A directed graphical model refers to the probability of random variables into a product of conditional probabilities, available for every node in the graph.
V-structures, “explaining away,” and learning directed graphical models
Here, you will understand more about directed graphical models, V-structures, and “explaining away”.
Inference in graphical models: Marginals and message passing
This chapter will teach you about inference in graphical models, such as Marginals and message passing.
Hidden Markov Model (HMM)
This chapter will brush your previous knowledge of the Hidden Markov Model (HMM).
Kalman filter
The Kalman filter algorithm is used to provide estimates of some unknown variables, given the measurements are observed over a particular period.

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 IDSS and 8 Continuing Education Units (CEUs)

Upon successful completion of the program, you will receive one of the best professional certificates in Data Science, for it will be from MIT Institute for Data, Systems, and Society (IDSS).

Projects and Case Studies

Following a “learn by doing” pedagogy, the Data Science and Machine Learning Program offers you the opportunity to construct your understanding through solving real-world case studies and practice activities. Below are samples of potential project topics and case studies.

Food

Foodhub

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Entertainment

Movies Recommendation System

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Transportation

NYC City Bus Ride

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Predicting Wages

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Automobile

Auto-mpg Analysis

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Space

The Challenger Disaster

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Biology

Genomic Data Clustering

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Data Scientist Employee Attrition

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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 and machine learning practitioners from leading global organizations.

Program Faculty

Munther Dahleh

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

John N. Tsitsiklis

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

Ankur Moitra

Rockwell International Career Development Associate Professor, Mathematics and IDSS, MIT

Caroline Uhler

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

David Gamarnik

Nanyang Technological University Professor of Operations Research, Sloan School of Management and IDSS, MIT

Devavrat Shah

Professor, EECS and IDSS, MIT

Guy Bresler

Associate Professor, EECS and IDSS, MIT

Jonathan Kelner

Professor, Mathematics, MIT

Kalyan Veeramachaneni

Principal Research Scientist at the Laboratory for Information and Decision Systems, MIT.

Philippe Rigollet

Professor, Mathematics and IDSS, MIT

Stefanie Jegelka

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

Tamara Broderick

Associate Professor, EECS and IDSS, MIT.

Victor Chernozhukov

Professor, Economics and IDSS, MIT

Program Mentors

Bradford Tuckfield

Founder and Data Science Consultant

Vaibhav Verdhan

Analytics Leader, Global Advanced Analytics

Mayan Murray

Senior Data Scientist and UX Consultant

Vibhor Kaushik

Data Scientist

Amit Agarwal

Senior Data Scientist

Kemal Yilmaz

Senior Data Scientist

Xiaojun Su

Data Science Product Manager

Juan Castillo

Machine Learning Engineer

Andrew Marlatt

Data Scientist - Revenue Expansion

Rohit Dixit

Senior Data Scientist

Srikanth Pyaraka

Data Science Product Manager

Angel Das

Data Science Consultant

Shirish Gupta

Lead Data Scientist

Vanessa Afolabi

Senior Data Scientist

Thinesh Pathmanathan

Data Scientist

Grivine Ochieng

Lead Data Engineer

Your Learning Experience

The Data Science and Machine Learning: Making Data-Driven Decisions Program is distinguished by its unique combination of MIT academic leadership, recorded lectures by MIT faculty, an application-based pedagogy, and personalized mentorship from industry experts.

LEARN WITH MIT FACULTY

Learn Data Science and Machine Learning with MIT Faculty

Self-paced program with recorded lectures from MIT faculty in Data Science & Machine Learning.
Program curriculum and design by world-renowned MIT faculty.
Position yourself as a data science leader by gaining industry-valued skills.

PERSONALIZED AND INTERACTIVE

Personalized Mentorship and Support

Weekly online mentorship from Data Science and Machine Learning experts.
Small groups of learners for personalized guidance and support.
Interact with like-minded peers from diverse backgrounds and geographies.
Dedicated Program Manager provided by Great Learning, for academic and non-academic queries.

View Experience

PRACTICAL AND HANDS-ON

Build your Data Science and Machine Learning Portfolio

Demonstrate Data Science leadership by building a portfolio of 3 industry-relevant projects and 50+ case studies.
Learn via practical applications to understand how data science and machine learning concepts translate into the real world.

Why Our Learners Choose the Data Science and Machine Learning Program

This is the first step in what I hope to be a very fruitful addition to my career in behavioral economics in health promotion and nutrition sciences. I strongly recommend this program to others who are looking to learn more about how to integrate data science and machine learning into your field of study. It’s completely worth it.

Monica Pampell

Health science Analyst, Food and Drug Administration, USA

The combination of the curriculum and relevant faculty to deliver it was a major stroke for the program, I really enjoyed the modules Neural Network and Deep Learning whereas recommendation systems and Predictive Analytics was challenging and exciting at the same time.

Kaase Gbakon

Senior Analyst, Ministry of Energy and Resource, Government of Sasketchewan

Learner Testimonials

My exposure to Data Science and Machine Learning Program was exceptional. The professors were phenomenal. They were patient and supported our learning with mentoring sessions and ample online video educational sessions throughout the course.

Lanetta Bronte-Hall

President and CEO Foundation for Sickle Cell Disease Research

Being an online program, one can have all the benefits of the flexible study and instant access to the rich resources through the advanced learning platform.

Rana Risheh

Developer-Director and Quality Supervisor Knowlgica

The instructors and coordinators were amazing, they always answered my questions and cleared my doubts. I am already using unsupervised machine learning algorithms in my robotics projects.

Salman Siddiqui

Control System Integrator Cherkam

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

Ratings & Reviews by learners

All Reviews

Mustafa Eltayeb Noureldin Ibrahim

16 Jan 2024

Batch of March 2023 at 249 Analytica | Oman

The program was amazing and informative. I learned so much from the experts. The best part was the online community of learners and mentors who supported me throughout the journey. If anyone wants to level up their data skills, I would highly recommend this program.

Ghadah Alkhadim

16 Jan 2024

Batch of January 2023 | PhD/Doctoral degree or Advanced graduate work at Educational Services | Saudi Arabia

The instructors teaching this program are experts and are very cooperative with the learners. The program is well-organized and motivating. It added knowledge to my existing background which is statistics. For anyone interested in developing analytical skills, I strongly recommend attending this workshop.

Sarah Shakir Hamad Al-Lami

16 Jan 2024

Batch of October 2021 | Finance Business Advisor at Shell - BGC (Basra Gas Company) at Basra Gas Company (partnership between Shell Oil & Energy and South Gas company in Iraq ) | Iraq

My journey of studying data science was very productive on the personal and professional levels. The program was a great resource and gave me a powerful background to start in a new domain. The professional way of providing program materials and mentoring sessions and the distribution of lectures and projects through the weeks was perfectly managed by the team. The way of communicating the concepts of lectures and the interactive explanation and application of algorithms, sharing best practices of methodologies was very helpful to perfectly achieve the goal of this program.

Carlos Andres Gomez Garcia

26 Oct 2023

Batch of October 2022 | Costa Rica

I am really satisfied with this program. As an Electronics Engineer, Data Science and Machine Learning was a new domain for me. The program focussed on the fundamentals of statistics. It also included several projects that allowed me to apply and practice the concepts with good data sets and real-world business problems. This was followed by mentored sessions where discussions happened and insights were shared.

Enrique Sequeira Cabrera

26 Oct 2023

Batch of August 2022 | Costa Rica

It was a really good program. It covered various topics and was also quite affordable. The Program Manager was always available to help me throughout the learning process. She was fast at responding to my queries.

Andrea Ruiz MillÃ¡n

26 Oct 2023

Batch of January 2022 | Mexico

My experience was pleasant. This program turned out to be very beneficial in my career. As my current doctoral work is focused on the domain of genetics, methods such as classifications and clustering are vital tools in my study. I study the characteristics that different DNA families have and then analyze the data obtained after making computational simulations by using molecular mechanics methods and quantum mechanics methods.

Jacques

15 Sep 2023

Batch of June 2022 | Netherlands

I really enjoyed the program. I enrolled in this program with the intention to obtain a clear understanding of Machine Learning. The Mentored Sessions served as a good way to review the video lectures and ask questions. The video lectures were quite comprehensive and the PDF handouts summary were incredibly helpful in sorting through the modules when needed. The assignments were very interesting and helped me get a better understanding of the concepts.

Ellie Vahidi

15 Sep 2023

Batch of June 2022 | United Kingdom

The program was very well organized and rich in scientific and practical content. The modules, examples, and projects were very useful and I still refer to them whenever needed. My Program Manager was very supportive and helpful. Whenever I ran into a problem or had a question, she was always there to help me out.

Mehran Vagheian

15 Sep 2023

Batch of June 2022 | Switzerland

This is one of the best programs I ever enrolled in. The modules and documents provided were very helpful and comprehensive. One of the best features of the program was the amazing academic support provided by the Program Manager. I would really recommend this program to everyone who is interested in Machine Learning and Deep Learning.

Vasu Bonda

15 Sep 2023

Batch of September 2022 | United Kingdom

This is an excellent program. It was well structured and ensured that learners from all professions and academic backgrounds could understand the concepts.

Richard Zuo

03 Dec 2023

Batch of August 2023 | United States

I am very pleased to see our program manager Vartica and our program leader Miguel (I hope I remember the name right), both supported me wholeheartedly during the 3 month program especially program manager my direct contact. I am extremely proud and happy we got three-year support after graduation. I am also happy about the capstone course about to commence. This education program is the BEST investment I have made in my life. My intellect was expanded by the MIT IDSS staffing, and it is unparalleled. I am proud of this academic achievement myself. Thank you our Program Manager and our Program Leader.

Tatenda Usai

30 Nov 2023

Batch of August 2023 | Front End Developer at Freelance | Zimbabwe

I had a wonderful learning experience and I would recommend this program to others

Read all reviews

Program Fees

Program Fees:
2,500 USD

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Payment Partners

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Benefits of learning from us

1Recorded lectures from world renowned MIT Faculty
2 Self-paced modules on ChatGPT and Generative AI
Live Mentorship from Data Science & Machine Learning Experts
3 industry-relevant projects and 50+ real-world case studies
Program Manager from Great Learning for Academic & Non-Academic Support

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Particular	Amount	Due by
Registration Fee	USD 2300	Immediately
TOTAL	USD 2300

Please note:

The applications follow a rolling process, which are closed when the requisite number of seats in the cohort are filled. To ensure your chances of securing a seat, we encourage you to apply as early as possible.
For more information about fee and payment, please write to us at dsml.mit@mygreatlearning.com.

Application Process

Fill the application form

Application Screening

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

Join 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: 18^th Apr 2024

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help@mygreatlearning.com

Cohort Start Dates

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18^th May 2024

Frequently Asked Questions

Program Details

What is the Data Science and Machine Learning course from the MIT Institute for Data, Systems, and Society (IDSS)?

The Data Science and Machine Learning course from MIT IDSS is designed in a modular structure with a comprehensive curriculum covering foundational and advanced concepts, which enables learners to master in-demand Data Science and Machine Learning skills to make data-driven decisions effectively.

Why should I choose this MIT Data Science and Machine Learning course?

At MIT IDSS, outstanding research is conducted with the objective of understanding and analyzing data to recommend solutions to complex societal problems. Consequently, the institute is dedicated to creating analytical techniques, including Statistics, Data Science, Machine Learning, etc., that may be employed in diverse areas like finance, health, urbanization, energy systems, and social networks.

MIT IDSS recognizes the power of uncovering the actual value of data and has created this Machine Learning and Data Science course for working professionals to advance their data analytical skills. Whether you are looking to enter into the sector, hunting for career development opportunities, or just looking to provide valuable insights to your business, the skills you develop in this course will make you familiar with harnessing the power of data in new and innovative ways.

What is the required weekly time commitment?

Each week involves 2 hours of recorded lectures and an additional 2 hours hands-on session each weekend for 7 weekends, which include hands-on practical applications and problem-solving. Additionally, based on your background, you should expect to invest between 2 to 4 hours every week to self-study and practice. So, that amounts to a time commitment of 8-12 hours per week.

What certificate will I receive after completing the MIT Data Science and Machine Learning course for working professionals?

Upon successfully completing this course, learners will secure a professional certificate, “Data Science and Machine Learning: Making Data-Driven Decisions”, from MIT IDSS.

Is the course completely virtual?

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

Who will teach this MIT Machine Learning and Data Science course for working professionals?

This course is taught by MIT faculty members who have several years of experience and are highly recommended. In addition to teaching faculty, the course also includes highly skilled industry mentors who guide you to work on hands-on projects via live and personalized mentoring sessions.

What languages and tools will I learn in this course?

Learners will master the most in-demand languages and tools during this course, including Python, NumPy, Keras, TensorFlow, Matplotlib, Scikit-Learn, and others.

What is unique about the curriculum of this MIT Machine Learning and Data Science course?

The curriculum of this course is designed by considering the following aspects:

Renowned MIT faculty members carefully crafted the curriculum to teach learners industry-relevant Data Science techniques and apply them to real-world problems.
The curriculum of this course covers essential Machine Learning techniques to deal with complex problems and make data-driven decisions.
Learners will also explore critical concepts of Deep Learning and Neural Networks and the process of applying them to areas like Natural Language Processing (NLP) and Computer Vision (CV).
The curriculum also teaches learners the theory behind Recommendation Systems and their application to diverse sectors.

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

MIT is ranked #1 university worldwide by QS World University Rankings 2023 and ranked #2 in national universities in the U.S. News & World Report 2023.

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

No, Data Science and Machine Learning: Making Data-Driven Decisions is an online professional certificate program offered by MIT IDSS (Institute for Data, Systems, and Society) 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 by the university.

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 the MIT Schwarzman College of Computing and IDSS.

What is the role of Great Learning in delivering this program?

This program is delivered in collaboration with Great Learning. Great Learning is a professional learning company with a global footprint in 170+ countries. Its mission is to make professionals around the globe proficient and future-ready. Great Learning collaborates with MIT IDSS and provides industry experts, student counselors, course support, and guidance to ensure students get hands-on training and live personalized mentorship on the application of concepts taught by the MIT IDSS faculty. Know More

Will I receive alumni status from MIT after completing my course?

Yes, learners will receive alumni status from MIT IDSS after completing their course. The benefits offered by MIT IDSS Alumni include:

Exclusive discounts on present and future courses offered by MIT IDSS
Subscription to MIT IDSS alumni mailing and newsletter lists
Advance notice on upcoming events and courses

What is the duration of this MIT Data Science and Machine Learning professional certificate course?

The duration of this course is 12 weeks, and the course contains recorded lectures from award-winning MIT faculty, 50+ real-world case studies, and 3 industry-relevant hands-on projects.

Is it mandatory to bring my own laptop?

The learners are required to bring their own laptops. But the necessary technology requirement shall be shared during registration.

Eligibility Criteria

What is the eligibility criteria for this MIT IDSS Data Science and Machine Learning course?

The eligibility criteria for this course are as follows:

Working professionals like early-career professionals or senior managers (IT Managers, Business Intelligence Analysts, Data Science Managers, Management Consultants, and Business Managers) who want to apply Data Science and Machine Learning techniques in their firms
Working professionals like Data Scientists, Data Analysts, or Business Analysts interested who wish to turn vast volumes of data into valuable insights
Entrepreneurs interested in innovation with the assistance of Data Science and Machine Learning techniques
Those possessing academic/professional training in Applied Statistics or Mathematics will find the course easier to learn. However, participants lacking this background will need to put in extra effort, and Great Learning will offer the required assistance

Fee Related Queries

What are my different payment options to register for the course on Data Science and Machine Learning online from MIT IDSS?

Candidates can pay the course fee through Bank Transfer and Credit/Debit Cards. They can also pay in easy instalments using PayPal credit options and get interest-free payments for up to 6 months (Note that these services are subject to credit approval by PayPal).

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

Are there any additional charges for purchasing books, virtual learning materials, or license fees?

No. All the requisite learning material is provided online to learners through the Learning Management System (LMS). Considering these fields are vast and constantly evolving, there is always more you can learn, and there will be a list of suggested books and other resources for your in-depth reading enjoyment.

Can my employer sponsor the program fee?

We accept corporate sponsorships and can assist you with the process.

[For more information, please write to us at dsml.mit@mygreatlearning.com]

What is the refund policy?

Please note that submitting the admission 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 registered.

Why Data Science and Machine Learning

How much salary can a Data Scientist and Machine Learning Specialist earn?

Varying data resonates with one fact: The average salary of a Data Scientist and a Machine Learning specialist is well over USD 100,000. Indeed recorded an average annual salary of USD 142,858 for Machine Learning specialists and USD 126,927 for Data Scientists in the US.

The list of industries incorporating data science and machine learning only continues to grow. The specialization commands authority across processes involved in Healthcare, Cybersecurity, Banking, Oil and Gas, Transportation, Education, Talent Acquisition, Inventory Management, Recommendation Systems and Price Optimization among other key business insights. With its inherently adaptive nature, the world of data science and analytics is here to stay and those mastering the tools and techniques of its stand to advance their careers while being at the forefront of all innovation.

What coding skills are needed to be a Data Scientist?

Complex algorithms and sophisticated tools make up a large part of a data scientist's day. In addition to data analysis tools, keeping up with the latest tools in data acquisition, data cleansing, data warehousing and data visualization is becoming increasingly important as the historically separate roles of data scientist and analyst become merged for increased efficiency. Python is the lingua franca of data science but knowledge of R, SAS, SQL, and sometimes Java, Scala, Julia among others must also be acquired at the foundational level itself. Technical soundness is a must for moving forward towards solutions while avoiding roadblocks.

How to become a Data Scientist?

A good academic background can always be a plus when you are embarking on your journey but there are certain specific skills that are essential to master the tools and techniques. The right combination of technical and non-technical skills is imperative to chisel out a career in Data Science and Machine Learning.

Revisiting your mathematical and statistical abilities can be a good motivator to further your journey into a data science and machine learning specialization. While you must be experienced with programming, hands-on experience with Python, R, etc. will mark the beginning of your transition into data science. Effective data science and machine learning course will not only commence with fulfilling these requirements for you but should be built to translate your learnings into a successful career with a comprehensive curriculum, also highlighting the role of a professional certificate in data science and machine learning.

What is the demand for data scientists?

Data Science roles have been among the most in-demand job roles in recent years. According to LinkedIn, hiring for data scientists saw an increase of 46% in the last year. The U.S Bureau of Labor Statistics has also predicted that the demand for data scientists is further expected to rise 27.9% by 2026.

So, we are well within the age of Big Data and the decision making that it drives across industries for business growth. From enhancing leisure experiences such as filtering Netflix recommendations to suggesting a direction for legal policies, Data Science and Machine Learning are at the core of it all. By making holistic concepts now measurable and predicting something as grand as “how people will live,” data science shines with immense possibilities. Yet, all this data, with its unparalleled potential, is of little use unless the right minds are at work to process it. Employers worldwide thus realize the importance of data scientists and machine learning specialists and even encourage a “culture of analytics” that we see emerging in workplaces today

What is the future of Data Science?

Organizations, on unlocking the potential of data science, have been known to see increased efficiency on several fronts. These include finding ways to reduce costs, expanding into new markets, tapping on different demographics, analysing the effectiveness of marketing campaigns and ultimately, deciding on the new products and services that can be launched. This resonated in Gartner predicting that, “By 2022, 90% of corporate strategies will explicitly mention information as a critical enterprise asset and analytics as an essential competency.”

What is machine learning?

Machine learning refers to a group of techniques used by data scientists that allow computers to learn from data. It is the underlying process allowing machines to learn from data which results in you getting all your recommendations and predictions from Alexa. From leisure to work, our lives are made easier with machine learning.

The responsibilities of a machine learning specialist constitute a spectrum extending from the creation of machine learning models to retraining systems. A specialization in machine learning means acquiring the necessary tools and techniques for the most crucial AI subset. A holistic skill set here, therefore, is made up of exceptional technical skills as well as an inherent learning attitude.

What is data science?

Data science is a field of study that employs a scientific approach to extract meaningful insights from data. Data science is a field that functions at more than one level. Meaningful insights are drawn from data sets, producing knowledge that helps in recommending apt actions for business growth. The knowledge derived from data science being at play is a combination of technology, statistics and trends in the business domain.

Why Data Science and Machine Learning?

The advancement of technology in various fields contributes greatly to the growth of industries. Hence, many businesses are using advanced Machine Learning and Data Science applications to draw the best outcomes. Let us understand a few of the benefits of these booming domains of technology.

Building better business strategies

By employing Data Science and Machine learning, organizations can develop the best business plan. Data Science and Machine learning provide solutions to come up with the best business plan that supports companies' exponential growth. Today, most of the top-notch companies are applying Data Science and Machine learning in projects and operation management to obtain better outcomes.

Better Research and Inventions

Organizations must be conscious of the latest trends in their market. A data-driven business team would shape their business in the best way that suits the requirements of end customers. A data-driven organization would learn current technological trends, plan a business strategy that delivers the best services. Businesses with a good vision and well versed in data can compose groundbreaking solutions. Data-backed approaches assist businesses to add value to their products by adapting themselves to the latest trends in the market by incorporating the latest technology.

Cost Reduction

Cost reduction is one of the major benefits that Data Science and Machine Learning contribute to any business. Small and medium scale companies certainly strive for their endurance considering their limited budget and resources. Data Science and Machine Learning help in formulating business solutions that are cost-effective.

Therefore, taking up a certification course in data science and machine learning would fetch you the best career opportunities with several industries in the market.

Registration Details

What is the registration process to pursue this online MIT Data Science and Machine Learning professional certificate course?

To enroll in this course, the applicants must meet the eligibility criteria mentioned earlier. The standard registration process for the eligible students is as follows:

Step-1: Applicants will need to complete their online application form.
Step-2: On receiving the application, the Great Learning program team will review it to determine your fit with the course.
Step-3: If selected, you will receive an offer for the upcoming cohort.
Step-4: Secure your seat by paying the fee.

What is the deadline to enroll in this Data Science and Machine Learning course from MIT IDSS?

The applications follow a rolling process, which is closed when the requisite number of seats in the cohort is filled. To ensure your chances of securing a seat, we encourage you to apply as early as possible.

Still have queries?
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Application Closes 18^th Apr 2024

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This program is delivered in collaboration with Great Learning. Great Learning is a professional learning company with a global footprint in 140+ countries. Its mission is to make professionals around the globe proficient and future-ready. Great Learning collaborates with MIT IDSS and provides industry experts, student counsellors, course support and guidance to ensure students get hands-on training and live personalized mentorship on the application of concepts taught by the MIT IDSS faculty. Accessibility.

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TOTAL	USD 2300

Please note:

The applications follow a rolling process, which are closed when the requisite number of seats in the cohort are filled. To ensure your chances of securing a seat, we encourage you to apply as early as possible.
For more information about fee and payment, please write to us at dsml.mit@mygreatlearning.com.

Data Science and Machine Learning: Making Data-Driven Decisions

Advance your Data Science Skills to solve business problems with this hand-made program for professionals

Program Delivered by:

In Collaboration with:

Why Join the Data Science and Machine Learning Program

Learn from renowned MIT Faculty

Personalized Mentorship and Support

Practical, Hands-on Training

Recorded lectures from 11 world-renowned MIT faculty and Instructors.

Personalized mentorship and guidance from data science & machine learning practitioners.

Work on 3 industry-relevant hands-on projects and 50+ case studies.

Curriculum covering Data Science, Machine Learning and more.

Who is this Program for?

Certificate of Completion from MIT IDSS

MIT IDSS Benefits:

World #1

MIT Rank in World Universities

U.S #2

MIT Rank in National Universities

Curriculum

Projects and Case Studies

Foodhub

Area of Project

Small Summary

Tools & Techniques used:

Movies Recommendation System

Area of Project

Small Summary

Tools & Techniques used:

NYC City Bus Ride

Area of Project

Small Summary

Tools & Techniques used:

Predicting Wages

Area of Project

Small Summary

Tools & Techniques used:

Auto-mpg Analysis

Area of Project

Small Summary

Tools & Techniques used:

The Challenger Disaster

Area of Project

Small Summary

Tools & Techniques used:

Genomic Data Clustering

Area of Project

Small Summary

Tools & Techniques used:

Data Scientist Employee Attrition

Area of Project

Small Summary

Tools & Techniques used:

MIT Faculty and Industry Experts

Your Learning Experience

Learn Data Science and Machine Learning with MIT Faculty

Personalized Mentorship and Support

Build your Data Science and Machine Learning Portfolio

Why Our Learners Choose the Data Science and Machine Learning Program

Monica Pampell

Kaase Gbakon

Learner Testimonials

Lanetta Bronte-Hall

Rana Risheh

Salman Siddiqui

Oluwarotimi Williams Samuel

Ratings & Reviews by learners

Program Fees

Benefits of learning from us

Application Process

Fill the application form

Application Screening

Join program

Upcoming Application Deadline

Deadline: 18th Apr 2024

Reach out to us

Cohort Start Dates

Online

Frequently Asked Questions

Still have queries? Contact Us

Learn from renowned
MIT Faculty

Deadline: 18^th Apr 2024

Still have queries?
Contact Us