The file errata contains a list of significant known errors in the first and second printings. The code for each chapter and any files used by the code are in the folder code files. To download all of the code, click
on the green button that says [Code]. That will download all of the files (as a zip file). MIT courses based on an earlier edition of this book can be found at:
 281 Pages · 2013 · 21.34 MB · 844 Downloads· English
Load more similar PDF files PDF Drive investigated dozens of problems and listed the biggest global issues facing the world today. Let's Change The World Together MIT Press, 2013 - Computers - 268 pages 0 Reviews Reviews aren't verified, but Google checks for and removes fake content when it's identified This book introduces students with little or no prior programming experience to theart of computational problem solving using Python and various Python libraries, including PyLab. Itprovides students with skills that will enable them to make productive use of computationaltechniques, including some of the tools and techniques of "data science" for usingcomputation to model and interpret data. The book is based on an MIT course (which became the mostpopular course offered through MIT's OpenCourseWare) and was developed for use not only in aconventional classroom but in in a massive open online course (or MOOC) offered by the pioneeringMIT--Harvard collaboration edX. Students are introduced to Python and the basics of programming inthe context of such computational concepts and techniques as exhaustive enumeration, bisectionsearch, and efficient approximation algorithms. The book does not require knowledge of mathematicsbeyond high school algebra, but does assume that readers are comfortable with rigorous thinking andnot intimidated by mathematical concepts. Although it covers such traditional topics ascomputational complexity and simple algorithms, the book focuses on a wide range of topics not foundin most introductory texts, including information visualization, simulations to model randomness,computational techniques to understand data, and statistical techniques that inform (and misinform)as well as two related but relatively advanced topics: optimization problems and dynamicprogramming. Introduction to Computation and Programming UsingPython can serve as a stepping-stone to more advanced computer science courses, or as abasic grounding in computational problem solving for students in other disciplines. |
Introduction to computation and programming using python second edition
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