Who should attend
- Working professionals who are interested in a career in Data Science and Machine Learning.
- Working professionals interested in leading Data Science and Machine Learning initiatives at their companies.
- Entrepreneurs interested in innovation using Data Science and Machine Learning.
About the course
Become a data-driven decision maker with live teaching from MIT faculty, hands-on projects, and mentorship from industry practitioners
- Learn from award-winning MIT IDSS faculty via live virtual sessions from the convenience of your home.
- Demonstrate data science leadership by building a portfolio of industryrelevant hands-on projects, including a 3 week long capstone project.
- Earn a certificate from MIT IDSS on your Applied Data Science skills.
- Get live mentorship from industry experts on the applications of concepts taught by faculty.
The program is 12-weeks long:
- 2 weeks for Foundational courses on Python and Statistical Science.
- 6 weeks of core curriculum including practical applications. Involves 50 hours of live sessions by MIT Faculty and Industry Experts, with hands-on practical applications and problem solving.
- 3 weeks for a final, integrative Capstone project.
- 1 week for project submissions
The Applied Data Science Bootcamp 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, NLP, Recommendation Systems, and more; it prepares you to lead data science efforts at any organisation.
In the first two weeks, we will cover the foundational concepts for data science that will form the building blocks of the course and help you sail through the rest of the journey with ease.
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
In the third week, you will learn about bootstrapping data to make it ML/AI ready, along with the practical applications of the techniques utilised.
Module 2: Data Analysis and Visualization
- Exploratory Data Analysis, Visualization (PCA, MDS and t-SNE) for visualization and batch correction
- Introduction to Unsupervised Learning: Clustering: hierarchical, k-means, 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 week, you will explore the fundamentals of Supervised Machine Learning and Prediction, including some key algorithms and widely-used techniques.
Module 3: Machine Learning
- Regression: Linear
- Classification: Logistic
- Model Evaluation: Cross Validation
In the fifth week of the bootcamp, 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
- Decision Trees
- Random Forest
- Time Series (Introduction)
This week will take you beyond traditional ML into the realm of Neural Nets and Deep Learning. You’ll learn how Deep Learning Networks can be successfully applied to areas such as Computer Vision, and more.
Module 5: Neural Networks, Deep Learning, and Computer Vision
- Intro to Deep Learning
- CNN and Vision
Learn about the different types of recommendation engines, how they are produced, and their specific applications to business use-cases.
Module 6: Recommendation Systems
- Intro to Recommendation Systems
- Recommendation Systems for Restaurants
- Recommendation Systems for Social Networks, Social Market Place
Learn about Graph Neural Networks, which generalize deep neural networks to graph unstructured data, and how they can be applied to Recommendation Systems
Module 7: Graph Neural Networks and Recommendation
- Graph Neural Networks (and Robustness)
- Tensor (time, hybrid), Neural Networks for Recommendation Systems
- Recommending Next Song for Music Streaming
This week is dedicated to the completion and final submission of learner projects.
Module 8: Project Week
Time for candidates to finish and submit their projects
The final three weeks of the bootcamp 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 9: Capstone Project
- Week 10: Milestone 1
- Week 11: Milestone 2
- Week 12: Synthesis + Presentation
Certificate from MIT IDSS
After This Course, You Will Be Able To
- 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 two major realms of Machine Learning, Deep Learning and Neural Networks, and how they can be applied to areas such as Computer Vision and NLP
- 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
Munther A. Dahleh is the William A. Coolidge Professor in the Department of Electrical Engineering and Computer Science at MIT, and the Director of the Institute for Data, Systems, and Society (IDSS). He is also affiliated with MIT’s Laboratory for Information and Decision Systems (LIDS). Dahleh...
Stefanie Jegelka is an X-Consortium Career Development Assistant Professor in the Department of Electrical Engineering and Computer Science at MIT, where she is a member of CSAIL, and affiliated with IDSS. Before joining MIT, she was a postdoctoral researcher at UC Berkeley. She obtained a Ph.D. ...
Devavrat Shah is a professor with the department of electrical engineering and computer science at MIT. He is a member of the Laboratory for Information and Decision Systems (LIDS) and Operations Research Center (ORC), and the Director of the newly formed Statistics and Data Center in Institute f...
Caroline Uhler joined the MIT faculty in 2015 as an assistant professor in EECS and IDSS. 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. Before joining M...
John N. Tsitsiklis was born in Thessaloniki, Greece, in 1958. He received the B.S. degree in Mathematics (1980), and the B.S. (1980), M.S. (1981), and Ph.D. (1984) degrees in Electrical Engineering, all from the Massachusetts Institute of Technology, Cambridge, Massachusetts, U.S.A. During the ac...
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