The purpose of the book is to guide the reader's preparation to crack the coding interviews. Preparing for the coding interview, not easy! Cracking the coding interivew? Like impossible for most of us! Luck does play a slight role sometimes in the outcome. So, just treating it as a learning process. No matter what the result is, there will be no regret.

Computer Science is really not just computer science. It is a combination of all fields; our normal interview problems fall into the eumerative combinatorics and our computer vision mostly is related to Linear Algebra. What really matters is our passion about learning more and applying it to solve real-life problems. How about forgetting about cracking the coding interviews, and just simply enjoy a ride that trying to seek the soul of the programming world. I'm just like you, I love to learn and I love to write and try to do a better job. No matter how much time it takes, because I love learning it!

There are plenty of books out there focusing on either teaching algorithmic knowledge (Introduction to Algorithms, Algorithmic Problem Solving, etc) or introducing interview process and solving interview problems(Cracking the Coding Interview--seriously, you are not likely to crack any interviews with this book, haha!, Coding Interview Questions, etc), but barely any that combines the two. This is a book designed to make up this role in the categorization; learning the algorithmic problem solving by analyzing and practicing interview problems -- a reciprocal relationship that makes 1+1=4: Algorithms, Interviews, Passion, and Confidence.

This is NOT a book that provides hiring statistic of each company or gives the reader quick tricks in order to pass a few coding interviews(T'm not good with NLP, 'cause I'm a computer vision person). Its purpose is to show you the beauty of the algorithimc problem solving in the hope that you will be more passionate and condifent about software engineering; the interview questions just set up a play ground where we strengthen what we learn. A strong candidate would be apt to sleep well before interviews, walk into the interviews and discuss his or her findings about the question from all possible angles and program the best solution for on the white-board. The interviews shall be a setting the interviewers and interviewees both have a nice and happy talk; learning from each other instead of a one-way relation -- tester and testee.

Related content will be posted in the form of medium publication and as a book in pdf instead of directly using markdown for better visual appearance and organization.

• Abstract Data Structures
• Discrete Programming
• Recurrence Relation

• Iteration and Recursion
• Bit Manipulation
• Python Data Structures

• PDF
• source code

• Complexity Analysis
• Search Strategies

• PDF
• source code: Graph Search
• source code: Tree Traversal

• Combinatorial Search

• PDF
• source code

• Reduce and Conquer
• Decrease and Conquer

• Binary Search, Binary Search Tree, and Segment Tree
• source code

• Sorting and Selection

• PDF
• source code: sorting algorithms
• source code: Python comparison and sort functions

• Dynamic Programming
• Greedy Algorithms

• Advanced Search on Linear Data Structures

• PDF
• source code

• Advanced Graph Algorithms
• Advanced Data Structures
• String Pattern Matches
• Math and Geometry Algorithms

• Dynamic Programming Questions (15%)
• Array Questions (15%)
• Linked List, Stack, Queue, and Heap Questions (12%)
• String Questions (15%)
• Tree Questions (10%)
• Graph Questions (15%)

Note: everything is still in progress, so use it with caution.

• Skiena, Steven S. The algorithm design manual: Text. Vol. 1. Springer Science & Business Media, 1998.

• T. H. Cormen, Introduction to algorithms, MIT press, 2009.

• Manber, Udi. Introduction to algorithms: a creative approach. Addison-Wesley Longman Publishing Co., Inc., 1989.

• Kleinberg, Jon, and Eva Tardos. Algorithm design. Pearson Education India, 2006.

• Russell, Stuart J., and Peter Norvig. Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited,, 2016. (Best book ever in explaining searching problem-solving, differentiate tree-search and graph-search)

• D. M. Beazley, Python essential reference, Addison-Wesley Professional,2009.

• S. Halim and F. Halim, Competitive Programming 3, Lulu Independent Publish, 2013.

• B. Slatkin, Effective Python: 59 Specific Ways to Write Better Python,Pearson Education, 2015.

• H. hua jiang, “Leetcode blogs,” https://zxi.mytechroad.com/blog/category, 2018, [Online; accessed 19-July-2018].

• B. Baka, “Python data structures and algorithms: Improve application performance with graphs, stacks, and queues,” 2017.

• “Competitive Programming,”https://cp-algorithms.com/, 2019, [Online; accessed 19-July-2018].

• “cs princeton,”https://aofa.cs.princeton.edu/60trees/, 2019, [Online; accessed 19-July-2018]

• Graph Visualize with graphviz. Examples. Tutorial to use Python

Practice is important. Schedule some mocking interviews with interviewing.io

If you have ideas to improve the book, about formatting, more contents, or correct the errors, do not hesitate to let me know.