This book uses Python 3 as its programming language due to its popularity and how easy it is to use in real coding interviews. The purpose of the book is to guide the reader's preparation for real-world software engineering, including but not limited to preparing and cracking coding interviews. 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, 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.

We guide the reader to master data structures, algorithm design and analysis, and problem-patterns through playing around with toy examples coded in Python thoroughly. Analyzing the behavior and computational complexity of more restricted and clearly-defined problems in the book helps lay the foundation of solving real-world problems which are more open and challenging.

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. 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.

I'm currently in full strength finalizing and finishing the book, it will likely to be published at the beignning of 2020. This repo is not well-maintenanced, but I will keep it up when I feel the content is nice and ready.

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.

Sample code used in each chapter of this part (only a small part right now).

• Introduction
• Fundamental Algorithm Design and Analysis (divide and conquer, complexity analysis),
• Data Structures(connecting the dots of data structures and Python built-in data types/Modules)
• Complete Search(searching on linear data structures, graphs, trees)
• Advanced Algorithm Design (Dynamic programming, greedy algorithms),
• Math and Bit Manipulation

LeetCode problems are used as exercises, only id and the title of the questions are given.

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

• Heap and Priority Queue

• Bit Manipulation

• Linear Data Structures

• Complete Search

• Binary Search
• Two Pointer Techniques
• Backtracking

• Tree Questions

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

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

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

3. 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)

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

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

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

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

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

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

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

If you like the example chapters that I put here, please leave your comment here. This book comes with a lot of hard work --personally, I paused my PhD program for a semester to focus on. Therefore, your comment is important to lead to the publication of the work.

If you have ideas to improve the book, about formatting, more contents, or correct the errors are also very welcome too.