- Do you want to learn basic concepts about NP-completeness?
- Are you interested in solving challenging and complex algorithmic problems?
- Do you have a prior knowledge and experience on algorithms and programming?
- Are you willing to seek professional help for this purpose?
- Are you finding it difficult to undergo training at an appropriate learning centre with efficient, qualified and experienced instructors?
 
The theoretical part of any science is based on mathematical methods of research. This also applies to computer science. It uses methods of mathematics to build and explore models of processing, transmission and use of information that creates the theoretical foundation on which the whole structure of computer science stands. 
 
This guide is beneficial for people – who want to solve various algorithmic problems after having prior knowledge on algorithms (CS215) and programming (CS101). The course is designed to acquaint students to promote the ideas of theoretical computer science. However, it is also useful both for researchers and programmers who wish to broaden their horizons.

This guide will help you learn:
 
- Challenging Problems - An introduction to tough problems and their analysis
- Understanding Hardness (What we mean when a problem is “hard” and the concept of NP-completeness)
- Showing Hardness - Tools to let you recognize and prove that a problem is hard
- Intelligent Force - Smart techniques to solve problems that should – theoretically – be impossible to solve
- Sloppy Solutions - Gaining speed by accepting approximate solutions
- Poking Around (Why randomness can be of help – sometimes?)
- An introduction to complexity classes
- Ultimate Limits - Problems that no computer can ever solve.

Author Bio
 
This efficient and effective guide has been designed and prepared by Sebastian Wernicke (currently working at with Seven Bridges Genomics after studying Bioinformatics at Universität Tübingen and holds a Ph.D. from Universität Jena in Germany), Sean Bennett (Course Architect at Udacity) and Sarah Norell (worked as a lecturer at the London School of Economics, University of Umeå and Mid-Sweden University after completing PhD in Mathematics from the University of London)