Description

The new version of an introductory textual content that teaches scholars the artwork of computational drawback fixing, masking subjects starting from easy algorithms to knowledge visualization.

This e-book introduces scholars with very little past programming enjoy to the artwork of computational drawback fixing the use of Python and more than a few Python libraries, together with PyLab. It supplies scholars with talents so that you can allow them to make efficient use of computational tactics, together with one of the gear and strategies of information technology for the use of computation to type and interpret knowledge. The e-book is according to an MIT path (which turned into the preferred path presented via MIT’s OpenCourseWare) and was once evolved to be used no longer handiest in a standard study room however in in a major open on-line path (MOOC). This new version has been up to date for Python 3, reorganized to make it more straightforward to make use of for classes that duvet just a subset of the fabric, and provides further subject matter together with 5 new chapters.

Students are presented to Python and the fundamentals of programming within the context of such computational ideas and strategies as exhaustive enumeration, bisection seek, and environment friendly approximation algorithms. Even supposing it covers such conventional subjects as computational complexity and easy algorithms, the e-book specializes in a variety of subjects no longer present in such a lot introductory texts, together with knowledge visualization, simulations to type randomness, computational tactics to take into account knowledge, and statistical tactics that tell (and lie to) in addition to comparable however rather complex subjects: optimization issues and dynamic programming. This version provides increased subject matter on data and gadget finding out and new chapters on Frequentist and Bayesian data.