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Data Science

Data Science introduces the essential methods for working with data, building from first principles to practical applications. Over four weeks, participants learn how to collect, organize, and analyze datasets using interactive notebooks and open-source tools. The course emphasizes understanding different modeling approaches, fitting data to functions, and exploring machine learning methods. By combining conceptual foundations with hands-on exercises, students gain both the vocabulary and the practice to critically evaluate data-driven work.

Curriculum

Overview

By the end of this track, participants will be able to:

Use Jupyter notebooks to collect, organize, and visualize data.
Apply basic statistical and mathematical models to fit and interpret datasets.
Distinguish between key modeling approaches (linear/nonlinear, supervised/unsupervised).
Implement simple machine learning workflows and evaluate their performance.
Communicate results through clear visualizations and explanations.

Outcomes

Analyze datasets with notebooks
Build and validate simple models
Work with complex data
Use neural networks to sort and analyze complex sets

Prerequisites

Basic Python recommended
Basic knowledge of algebra and functions (no advanced math required)
Curiosity about working with data across different fields

Site requirements

Jupyter (cloud or local)
Reliable internet

Global

October 21, 23, 28, 30 and November 4, 6, 11, 13 — 9:00–10:30A EDT

Local

Mon, Wed, Fri (local)

Local nodes set times

Faculty

Neil Gershenfeld

published

Director, MIT Center for Bits and Atoms

Prof. Neil Gershenfeld is the Director of MIT's Center for Bits and Atoms, where his unique laboratory is breaking down boundaries between the digital and physical worlds, from pioneering quantum computing to digital fabrication to the Internet of Things. He has been elected a Member of the National Academy of Engineering, a Fellow of the American Association for the Advancement of Science and of the American Physical Society, has been named one of Scientific American's 50 leaders in science and technology, as one of 40 Modern-Day Leonardos by the Museum of Science and Industry, one of Popular Mechanic's 25 Makers, has been selected as a CNN/Time/Fortune Principal Voice, and by Prospect/Foreign Policy as one of the top 100 public intellectuals. He's the founder of a global network of over 2500 fab labs in 150 countries, chairs the Fab Foundation, and leads the Fab Academy.

Website