6.00.2x: Introduction to Computational Thinking and Data Science


An introduction to using computation to understand real-world phenomena.

About this Course

6.00.2x is aimed at students with some prior programming experience in Python and a rudimentary knowledge of computational complexity. We have chosen to focus on breadth rather than depth. The goal is to provide students with a brief introduction to many topics, so that they will have an idea of what’s possible when the time comes later in their career to think about how to use computation to accomplish some goal. That said, it is not a “computation appreciation” course. Students will spend a considerable amount of time writing programs to implement the concepts covered in the course. Topics covered include plotting, stochastic programs, probability and statistics, random walks, Monte Carlo simulations, modeling data, optimization problems, and clustering.

What will I learn? If you successfully complete this course, you will have:

  • Developed some insight into the process of moving from an ambiguous problem statement to a computational formulation of a method for solving the problem,
  • Learned a useful set of algorithmic and problem reduction techniques,
  • Learned how to use simulations to shed light on problems that don’t easily succumb to closed form solutions,
  • Learned how to use computational tools, including simple statistical, machine learning, and plotting tools, to model and understand data.

All required readings are available within the courseware, courtesy of The MIT Press. A print version of the course textbook, Introduction to Computer Science and Programming Using Python, is also available for purchase. The MIT Press is offering enrolled students a special 30% discount on books ordered directly through the publisher’s website. To take advantage of this offer, please use promotion code guttag30 at The MIT Press.


Course Staff

  • Eric Grimson

    W. Eric L. Grimson is the Chancellor of the Massachusetts Institute of Technology, a professor of computer science and engineering, and the Bernard M. Gordon Professor of Medical Engineering. He was named Chancellor of MIT in 2011. A member of the MIT faculty since 1984, Professor Grimson previously served as head of the Department of Electrical Engineering and Computer Science, as its associate department head, and as its education officer. Professor Grimson is internationally recognized for his research in computer vision, especially in applications in medical image analysis. He and his students have developed techniques for activity and behavior recognition, object and person recognition, image database indexing, image guided surgery, site modeling, and many other areas of computer vision. Professor Grimson has been actively engaged with students throughout his career. For 25 years he lectured subject 6.001 Structure and Interpretation of Computer Programs, and is now engaged in teaching 6.00 Introduction to Computer Science and Programming and 6.01 Introduction to EECS. He has also taught undergraduate subjects in computer architecture, software engineering, and signal processing. In all, Professor Grimson has taught more than 10,000 MIT undergraduates and served as the thesis supervisor to almost 50 MIT PhDs. Professor Grimson is a native of Saskatchewan, Canada. He received the BSc (Hons) degree in mathematics and physics from the University of Regina in 1975 and his PhD in mathematics in 1980 from MIT. He is a recipient of the Bose Award for Excellence in Teaching in the School of Engineering at MIT. He is a fellow of the Association for the Advancement of Artificial Intelligence (AAAI) and a fellow of the Institute of Electrical and Electronics Engineers (IEEE).

  • John Guttag

    Professor Guttag is the Dugald C. Jackson Professor of Computer Science and Electrical Engineering at MIT. He leads the Computer Science and Artificial Intelligence Laboratory’s Data Driven Medical Research Group. The group works on the application of advanced computational techniques to medicine. Current projects include prediction of adverse medical events, prediction of patient-specific response to therapies, non-invasive monitoring and diagnostic tools, and tele-medicine. He has also done research, published, and lectured in the areas of data networking, sports analytics, software defined radios, software engineering, and mechanical theorem proving.

    Professor Guttag received his bachelors degree in English and his master's in applied mathematics from Brown University. His doctorate is from the University of Toronto.

    From January of 1999 through August of 2004, Professor Guttag served as Head of MIT’s Electrical Engineering and Computer Science Department. He is a Fellow of the ACM and a member of the American Academy of Arts and Sciences.

  • Ana Bell

    Ana Bell is a lecturer in the Computer Science and Electrical Engineering Department at MIT.

    Professor Bell received her Bachelor in Applied Science from the University of British Columbia in Vancouver, Canada. She received her MA and PhD from Princeton University. Her research was in computational biology, specifically using computational techniques to answer the questions: what do genes do, and how do genes interact with each other and other small molecules? 

    She discovered her passion for teaching after being appointed as a teaching assistant for two semesters for Introduction to Computer Science, at Princeton University. Since then, she has sought any opportunity to introduce students to the wonderful world of computer science!

  • 2014年10月21日, 9 星期
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  • 语言: 英语 Gb



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