Applied Data Science Program
offered by MIT University

Private University
168 acre campus
Estd. 1861

Applied Data Science Program
at
MIT University
Overview

Develop strong foundations in Python, mathematics, and statistics for data science

Duration	12 weeks
Total fee	₹2.88 Lakh
Mode of learning	Online
Course Level	UG Certificate

Applied Data Science Program
at
MIT University
Highlights

Earn a certificate of completion from MIT

Applied Data Science Program
at
MIT University
Course details

Skills you will learn

Python Statistics Data visualization Data analysis Data Science

Who should do this course?

For Professionals who are interested in a career in Data Science and Machine Learning
For Professionals interested in leading Data Science and Machine Learning initiatives at their companies
For Entrepreneurs interested in innovation using Data Science and Machine Learning

What are the course deliverables?

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
Understand the theory behind recommendation systems and explore their applications to multiple industries and business contexts

More about this course

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
In order to help you unravel the true worth of data, MIT Professional Education offers Applied Data Science Program, which aims to prepare data-driven decision makers for the future

Applied Data Science Program
at
MIT University
Curriculum

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

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

Machine Learning

Introduction to Supervised Learning -Regression

Model Evaluation- Cross Validation and Bootstrapping

Introduction to Supervised Learning-Classification

Practical Data Science

Decision Trees

Random Forest

Time Series (Introduction)

Deep learning

Intro to Neural Networks

Convolutional Neural Networks

Graph Neural Networks

Recommendation Systems

Intro to Recommendation Systems

Matrix

Tensor, NN for Recommendation Systems

Capstone Project

Week 10: Milestone 1

Week 11: Milestone 2

Week 12: Synthesis + Presentation

Applied Data Science Program
at
MIT University
Faculty details

Devavrat Shah

Devavrat Shah is a professor with the department of electrical engineering and computer science, MIT. He is a member of the Laboratory for Information and Decision Systems (LIDS) and Operations Research Center (ORC), and the Director of the Statistics and Data Science Center (SDSC) in IDSS.

Stefanie Jegelka

Stefanie Jegelka is an X-Consortium Career Development Associate Professor in the Department of Electrical Engineering and Computer Science at MIT, where she is a member of CSAIL, and affiliated with IDSS.

Munther Dahleh

Munther Dahleh is director of the Institute for Data, Systems, and Society. He was previously the associate department head of EECS. He is also a member of MIT’s Laboratory for Information and Decision Systems (LIDS).

Caroline Uhler

Caroline Uhler is an assistant professor in EECS and IDSS at MIT. She holds an MSc in Mathematics, a BSc in Biology, and an MEd in High School Mathematics Education from the University of Zurich. She obtained her PhD in Statistics from UC Berkeley in 2011.