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

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Lead Instructor(s)
Date(s)
Jan 14 - Apr 15, 2023
Mar 18 - Jun 17, 2023
Location
Live Virtual
Course Length
12 Weeks
Course Fee
$3,900
CEUs
3 CEUs
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Data is getting created at a rapid pace. It is estimated that more than 2 quintillion bytes of data have been created each day in the last two years. As organizations experience an overflow of data, they are sparing no effort to extract meaningful insights to make smarter business decisions. In order to help you unravel the true worth of data, MIT Professional Education offers the Applied Data Science Program, which aims to prepare data-driven decision makers for the future.

THIS program MAY BE TAKEN AS A STANDALONE PROGRAM OR AS PART OF THE PROFESSIONAL CERTIFICATE PROGRAM IN MACHINE LEARNING & ARTIFICIAL INTELLIGENCE. COMPLETING THE COURSE WILL CONTRIBUTE 5 DAYS TOWARDS THE CERTIFICATE.

Program Overview

In this 12-week program, you will be able to upgrade your data analytics skills by learning the theory and practical application of supervised and unsupervised learning, time-series analysis, neural networks, recommendation engines, regression, and computer vision, to name a few.

Upon successful fulfillment of requirements, you will receive a certificate of completion from MIT Professional Education at the end of the program.

Program Mentors

The program coaches you to work on hands-on industry relevant projects by data science and machine learning experts via live and personalized mentoring and learning sessions to give you a practical understanding of core concepts.

  • Subhodeep Dey - Data Scientist (Project Lead), UnitedHealth Group (India)
  • Joseph Deutsch – Data Scientist, Capital One (United States)
  • Kalle Bylin - Product Engineer, Modyo (Colombia)
  • George Liu – Senior Data Scientist, Chatter Research (Canada Area)
  • Vaibhav Verdhan - Principal Data Scientist, Johnson & Johnson (Ireland)

Contact Great Learning for more information at adsp.mit@mygreatlearning.com or call +1 617 468 7899 / +91 9606 053 237.

Learning Outcomes
  • Understand the intricacies of data science techniques and their applications to real-world problems.
  • Implement various machine learning techniques to solve complex problems and make data-driven business decisions.
  • Explore the realms of Machine Learning, Deep Learning, and Neural Networks, and how they can be applied to areas such as Computer Vision.
  • Develop strong foundations in Python, mathematics, and statistics for data science.
  • Understand the theory behind recommendation systems and explore their applications to multiple industries and business contexts.
  • Build an industry-ready portfolio of projects to demonstrate your ability to extract business insights from data.

 

Who Should Attend
  • Professionals who are interested in a career in Data Science and Machine Learning.
  • Professionals interested in leading Data Science and Machine Learning initiatives at their companies.
  • Entrepreneurs interested in innovation using Data Science and Machine Learning.

Prerequisites: Basic knowledge of Computer Programming and Statistics

Testimonials

MIT professors gave a high-level overview on the theory side of different algorithms and approached explanations with examples. The weekly quizzes and projects helped reinforce techniques learned, and the capstone project provided end-to-end hands-on experience. After this program, I have a much better understanding of the fundamental ML algorithms. It has set a solid foundation for my Data Science career. I would recommend it to anyone who wants to upskill and succeed in the AI/Data Science field.
Huajuan Jane Zou (Senior Data Scientist, Aetna, a CVS Health Company)
“The Applied Data Science Program was really helpful for me to learn many fundamental concepts of Machine Learning and Data Science. The structure and sustained pace of this program helped me finish the wide breadth of topics covered. The live lectures from MIT professors were the cherry on top. The program support and mentoring sessions were very helpful. The final project was a good hands-on experience and gave me confidence to take up real-life projects.”
Archana Chaudhary Senior Engineering Manager, Adobe Systems
“The impetus behind enrolling in ADSP was primarily a curiosity to learn and a desire to get better,” Petway says. “In my time in pro and college sports, we’ve had whole departments dedicated to data science, so I know it’s a skill set I’ll need in the future.”
Adam Petway, Director of strength and conditioning for men's basketball at the University of Louisville
Program Curriculum 

MIT Professional Education's Applied Data Science Program, with curriculum developed and taught by MIT faculty, is delivered in collaboration with Great Learning.  Unique to the Applied Data Science Program is a dedicated Program Manager, provided by Great Learning, who will be your single point of contact for all academic and non-academic queries in the program. They will keep track of your learning journey, give you personalized feedback, and the required nudges to ensure your success.

Program Curriculum 

The program is 12 weeks long:

  • 2 weeks for foundations
  • 6 weeks of core curriculum, including practical applications
  • 1 week for project submissions
  • 3 weeks for a final, integrative Capstone project

Week 1&2 - Module 1

Foundations for Data Science

  • Python Foundations - Libraries: Pandas, NumPy, Arrays and Matrix handling, Visualization, Exploratory Data Analysis (EDA)
  • Statistics Foundations: Basic/Descriptive Statistics, Distributions (Binomial, Poisson, etc.), Bayes, Inferential Statistics

Week 3 - Module 2

Data Analysis & Visualization

  • Exploratory Data Analysis, Visualization (PCA, MDS and t-SNE) for visualization and batch correction 
  • Introduction to Unsupervised Learning: Clustering includes - Hierarchical, 
  • K-Means, DBSCAN, Gaussian Mixture
  • Networks: Examples (data as a network versus network to represent dependence among variables), determine important nodes and edges in a network, clustering in a network

Week 4 - Module 3

Machine Learning

  • Introduction to Supervised Learning -Regression
  • Model Evaluation- Cross Validation and Bootstrapping
  • Introduction to Supervised Learning-Classification

Week 5 - Module 4

Practical Data Science

  • Decision Trees
  • Random Forest
  • Time Series (Introduction)

Week 6 - Learning Break

Week 7 - Module 5

Deep learning

  • Intro to Neural Networks
  • Convolutional Neural Networks
  • Graph Neural Networks

Week 8 - Module 6

Recommendation Systems

  • Intro to Recommendation Systems
  • Matrix
  • Tensor, NN for Recommendation Systems

Week 9 - Project Week

Time for participants to finish and submit their projects

Week 10-12 - Module 7 

Capstone Project

  • Week 10: Milestone 1
  • Week 11: Milestone 2
  • Week 12: Synthesis + Presentation