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Introduction to Computational Thinking and Data Science

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CONTENT
Instructors
Prof. Eric Grimson & Prof. John Guttag & Dr. Ana Bell
EXPLANATION

Grounded in computational thinking, provides a solid foundation for entering the field of data science by equipping students with fundamental skills.

SUBJECTS

Introduction to Computational Thinking and Data Science

  • Course Description

Course Videos

  • Lecture 1: Introduction and Optimization Problems

  • Lecture 2: Optimization Problems

  • Lecture 3: Graph-Theoretic Models

  • Lecture 4: Stochastic Thinking

  • Lecture 5: Random Walks

  • Lecture 6: Monte Carlo Simulation

  • Lecture 7: Confidence Intervals

  • Lecture 8: Sampling and Standard Error

  • Lecture 9: Understanding Experimental Data

  • Lecture 10: Introduction to Machine Learning

  • Lecture 11: Clustering

  • Lecture 12: Classification

  • Lecture 13: Classification and Statistical Sins

  • Lecture 14: Statistical Sins and Wrap Up

EDUCATION DETAILS

About This Course

The Introduction to Computational Thinking and Data Science course is specifically designed for students who are new to programming or have no prior experience. The primary goal of the course is to demonstrate the power of computation in problem-solving and to help students build confidence by showing that they can achieve meaningful results through small yet effective programs.

Using the Python programming language, this course appeals not only to those pursuing technical fields such as Computer Science or Electrical Engineering but to students from all disciplines. By taking this course, participants will gain the ability to take on programming-based tasks in research projects and will build a solid foundation for success in advanced courses across numerical and data-driven fields such as data science.


Prerequisites

This course is considered the second-semester continuation of the Introduction to Computer Science and Programming Using Python course. It is strongly recommended to complete that course beforehand.


Instructors

Prof. Eric Grimson is a professor of Computer Science and Electrical Engineering at MIT, where he also served as Chancellor for many years. He is well-known for his work in artificial intelligence and computer vision. Grimson is highly influential in teaching; with his clear, structured, and accessible style, he teaches one of MIT’s most widely viewed computer science courses.

Prof. John Guttag is a senior professor in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). He has conducted extensive research in system biology, health informatics, and machine learning, and is considered a pioneer of computational thinking in engineering. As one of the architects of MIT’s introductory computer science curriculum, he is especially recognized for his problem-solving-oriented teaching approach.

Dr. Ana Bell is a senior lecturer in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL).
She is particularly known for her contributions to the Introduction to Computer Science and Programming Using Pythoncourses. Dr. Bell has played a key role in creating popular online learning materials published on MIT OpenCourseWare and MITx (edX).

Her teaching focuses on making fundamental programming concepts clear, systematic, and effective. She specializes in artificial intelligence, data structures, algorithms, and programming education, designing learning materials that reach a broad and diverse audience.

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