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Free hands-on course with the implementation (in Python) and description of several computational, mathematical and statistical algorithms.

Although it is not intended to have the formal rigor of a book, we tried to be as faithful as possible to the original algorithms and methods, only adding variants, when these were necessary for didactic purposes.

The best way to get the most out of this course is to carefully read each selected problem, try to think of a possible solution (language independent) and then look at the proposed Python code and try to reproduce it in your favorite IDE. If you already have knowledge of the Python language, then you can go directly to programming your solution and then compare it with the one proposed in the course.

If you want to play with these notebooks online without having to install any library or configure hardware, you can use the following service:

In mathematics and computer science, an algorithm is a finite sequence of well-defined, computer-implementable instructions, typically to solve a class of problems or to perform a computation.

  conda install -c anaconda numpy  conda install -c anaconda pymc  conda install -c anaconda networkx

• G. Brassard, P. Bratley. (2006).Fundamentals of Algorithmics. Englewood Cliffs, New Jersey: Prentice-Hall, Inc.
• R.C.T. Lee, S.S. Tseng, R.C. Chang, Y.T.Tsai. (2005).Introduction to the Design and Analysis of Algorithms. A Strategic Approach. Asia: McGraw-Hill Education.
• K. Rosen. (2012).Discrete Mathematics and Its Applications. NewYork, NY: McGraw-Hill Education; Edición: 7th.

Any kind of feedback/suggestions would be greatly appreciated (algorithm design, documentation, improvement ideas, spelling mistakes, etc...). If you want to make a contribution to the course you can do it through a PR.

Please read thecontributing andcode of conduct documentation.

• Created by Andrés Segura-Tinoco
• Created on May 17, 2019

This project is licensed under the terms of theMIT license.