algorithmic problem solving with python pdf

• Title Algorithmic Problem Solving with Python
• Author(s) John B. Schneider , Shira Lynn Broschat, Jess Dahmen
• Publisher: Washington State University (February 22, 2019. Updated Continuously)
• License(s): Creative Commons Attribution-ShareAlike 4.0 International License
• Hardcover/Paperback N/A
• eBook PDF Files and a single PDF (360 pages, 2.1 MB)
• Language: English
• ISBN-10: N/A
• ISBN-13: N/A

This book uses Python to introduce folks to programming and algorithmic thinking. It is sharply focused on classical algorithms, but it also gives a solid understanding of fundamental algorithmic problem-solving techniques.

The book deals with some of the most important and challenging areas of programming and computer science in a highly readable manner. It covers both algorithmic theory and programming practice, demonstrating how theory is reflected in real Python programs.

Well-known algorithms and data structures that are built into the Python language are explained, and the user is shown how to implement and evaluate others.

If you are a Python lover and looking to learn EVERYTHING you need to learn about the Python approach to algorithm design and analysis, this book is exactly what you need.

• John B. Schneider is an Associate Professor of Electrical Engineering & Computer Science at Washington State University.
• Python Programming
• Algorithms and Data Structures
• Computational Complexity
• Algorithmic Problem Solving with Python (John B. Schneider, et al)
• The Mirror Site (1) - Videos Lectures
• The Mirror Site (2) - PDF
• Book Homepage (Code and Data, Solutions, Video, etc.)

This book covers Analysis and Design of Algorithms, Scientific Computing, Monte Carlo Simulations, and Parallel Algorithms. It teaches the core knowledge required by any scientist interested in numerical algorithms and computational finance.

This is a textbook about computer science. It is also about Python. However, there is much more. The tools and techniques that you learn here will be applied over and over as you continue your study of computer science.

It promotes object-oriented design using Python and illustrates the use of the latest object-oriented design patterns. Virtually all the data structures are discussed in the context of a single class hierarchy.

Learn how to use Python to write programs that do in minutes what would take you hours to do by hand - no prior programming experience required. You'll create Python programs that effortlessly perform useful and impressive feats of automation.

This hands-on guide takes you through the Python programming language a step at a time, beginning with basic programming concepts before moving on to functions, recursion, data structures, and object-oriented design. 2nd edition updated for Python 3.

It focuses on introducing programming techniques and developing good habits. To that end, our approach avoids some of the more esoteric features of Python and concentrates on the programming basics that transfer directly to other imperative programming.

This book deepens your knowledge of problem-solving techniques from the realm of computer science by challenging you with time-tested scenarios, exercises, and algorithms. As you work through examples in search, clustering, graphs, and more.

The algorithmic approach to solving problems in computer technology is an essential tool. This book presents a readable, entertaining, and energetic book that will motivate and challenge students to open their minds to the algorithmic nature of problem solving.

• IT Research Library
• Books by O'Reilly®
• Pro Certificates Studies
• Careers and Job Interviews
• Project Management
• Search Engines
• Developer Tools
• Bargin Computer Books
• Free IT Magazines
• About This Site

• Table of Contents
• Scratch ActiveCode
• Navigation Help
• Help for Instructors
• About Runestone
• Report A Problem
• 1. Introduction
• 2. Analysis
• 3. Basic Data Structures
• 4. Recursion
• 5. Sorting and Searching
• 6. Trees and Tree Algorithms
• 7. Graphs and Graph Algorithms

Problem Solving with Algorithms and Data Structures using Python ¶

By Brad Miller and David Ranum, Luther College (as remixed by Jeffrey Elkner)

• 1.1. Objectives
• 1.2. Getting Started
• 1.3. What Is Computer Science?
• 1.4. What Is Programming?
• 1.5. Why Study Data Structures and Abstract Data Types?
• 1.6. Why Study Algorithms?
• 1.7. Review of Basic Python
• 1.8.1. Built-in Atomic Data Types
• 1.8.2. Built-in Collection Data Types
• 1.9.1. String Formatting
• 1.10. Control Structures
• 1.11. Exception Handling
• 1.12. Defining Functions
• 1.13.1. A Fraction Class
• 1.13.2. Inheritance: Logic Gates and Circuits
• 1.14. Summary
• 1.15. Key Terms
• 1.16. Discussion Questions
• 1.17. Programming Exercises
• 2.1. Objectives
• 2.2. What Is Algorithm Analysis?
• 2.3. Big-O Notation
• 2.4.1. Solution 1: Checking Off
• 2.4.2. Solution 2: Sort and Compare
• 2.4.3. Solution 3: Brute Force
• 2.4.4. Solution 4: Count and Compare
• 2.5. Performance of Python Data Structures
• 2.7. Dictionaries
• 2.8. Summary
• 2.9. Key Terms
• 2.10. Discussion Questions
• 2.11. Programming Exercises
• 3.1. Objectives
• 3.2. What Are Linear Structures?
• 3.3. What is a Stack?
• 3.4. The Stack Abstract Data Type
• 3.5. Implementing a Stack in Python
• 3.6. Simple Balanced Parentheses
• 3.7. Balanced Symbols (A General Case)
• 3.8. Converting Decimal Numbers to Binary Numbers
• 3.9.1. Conversion of Infix Expressions to Prefix and Postfix
• 3.9.2. General Infix-to-Postfix Conversion
• 3.9.3. Postfix Evaluation
• 3.10. What Is a Queue?
• 3.11. The Queue Abstract Data Type
• 3.12. Implementing a Queue in Python
• 3.13. Simulation: Hot Potato
• 3.14.1. Main Simulation Steps
• 3.14.2. Python Implementation
• 3.14.3. Discussion
• 3.15. What Is a Deque?
• 3.16. The Deque Abstract Data Type
• 3.17. Implementing a Deque in Python
• 3.18. Palindrome-Checker
• 3.19. Lists
• 3.20. The Unordered List Abstract Data Type
• 3.21.1. The Node Class
• 3.21.2. The Unordered List Class
• 3.22. The Ordered List Abstract Data Type
• 3.23.1. Analysis of Linked Lists
• 3.24. Summary
• 3.25. Key Terms
• 3.26. Discussion Questions
• 3.27. Programming Exercises
• 4.1. Objectives
• 4.2. What Is Recursion?
• 4.3. Calculating the Sum of a List of Numbers
• 4.4. The Three Laws of Recursion
• 4.5. Converting an Integer to a String in Any Base
• 4.6. Stack Frames: Implementing Recursion
• 4.7. Introduction: Visualizing Recursion
• 4.8. Sierpinski Triangle
• 4.9. Complex Recursive Problems
• 4.10. Tower of Hanoi
• 4.11. Exploring a Maze
• 4.12. Dynamic Programming
• 4.13. Summary
• 4.14. Key Terms
• 4.15. Discussion Questions
• 4.16. Glossary
• 4.17. Programming Exercises
• 5.1. Objectives
• 5.2. Searching
• 5.3.1. Analysis of Sequential Search
• 5.4.1. Analysis of Binary Search
• 5.5.1. Hash Functions
• 5.5.2. Collision Resolution
• 5.5.3. Implementing the Map Abstract Data Type
• 5.5.4. Analysis of Hashing
• 5.6. Sorting
• 5.7. The Bubble Sort
• 5.8. The Selection Sort
• 5.9. The Insertion Sort
• 5.10. The Shell Sort
• 5.11. The Merge Sort
• 5.12. The Quick Sort
• 5.13. Summary
• 5.14. Key Terms
• 5.15. Discussion Questions
• 5.16. Programming Exercises
• 6.1. Objectives
• 6.2. Examples of Trees
• 6.3. Vocabulary and Definitions
• 6.4. List of Lists Representation
• 6.5. Nodes and References
• 6.6. Parse Tree
• 6.7. Tree Traversals
• 6.8. Priority Queues with Binary Heaps
• 6.9. Binary Heap Operations
• 6.10.1. The Structure Property
• 6.10.2. The Heap Order Property
• 6.10.3. Heap Operations
• 6.11. Binary Search Trees
• 6.12. Search Tree Operations
• 6.13. Search Tree Implementation
• 6.14. Search Tree Analysis
• 6.15. Balanced Binary Search Trees
• 6.16. AVL Tree Performance
• 6.17. AVL Tree Implementation
• 6.18. Summary of Map ADT Implementations
• 6.19. Summary
• 6.20. Key Terms
• 6.21. Discussion Questions
• 6.22. Programming Exercises
• 7.1. Objectives
• 7.2. Vocabulary and Definitions
• 7.3. The Graph Abstract Data Type
• 7.4. An Adjacency Matrix
• 7.5. An Adjacency List
• 7.6. Implementation
• 7.7. The Word Ladder Problem
• 7.8. Building the Word Ladder Graph
• 7.9. Implementing Breadth First Search
• 7.10. Breadth First Search Analysis
• 7.11. The Knight’s Tour Problem
• 7.12. Building the Knight’s Tour Graph
• 7.13. Implementing Knight’s Tour
• 7.14. Knight’s Tour Analysis
• 7.15. General Depth First Search
• 7.16. Depth First Search Analysis
• 7.17. Topological Sorting
• 7.18. Strongly Connected Components
• 7.19. Shortest Path Problems
• 7.20. Dijkstra’s Algorithm
• 7.21. Analysis of Dijkstra’s Algorithm
• 7.22. Prim’s Spanning Tree Algorithm
• 7.23. Summary
• 7.24. Key Terms
• 7.25. Discussion Questions
• 7.26. Programming Exercises

Acknowledgements ¶

We are very grateful to Franklin Beedle Publishers for allowing us to make this interactive textbook freely available. This online version is dedicated to the memory of our first editor, Jim Leisy, who wanted us to “change the world.”

Indices and tables ¶

• Module Index
• Search Page

School of Electrical Engineering and Computer Science Washington State University

Algorithmic problem solving with python.

• Even more »

Account Options

• Try the new Google Books
• Advanced Book Search

Get this book in print

• Barnes&Noble.com
• Books-A-Million
• Find in a library
• All sellers  »

Selected pages

Other editions - View all

Common terms and phrases, about the author  (2020), bibliographic information.

• Table of Contents
• Course Home
• Assignments
• Peer Instruction (Instructor)
• Peer Instruction (Student)
• Change Course
• Instructor's Page
• Progress Page
• Edit Profile
• Change Password
• Scratch ActiveCode
• Scratch Activecode
• Instructors Guide
• About Runestone
• Report A Problem
• This Chapter
• 1. Introduction' data-toggle="tooltip" >

Problem Solving with Algorithms and Data Structures using Python ¶

By Brad Miller and David Ranum, Luther College

There is a wonderful collection of YouTube videos recorded by Gerry Jenkins to support all of the chapters in this text.

• 1.1. Objectives
• 1.2. Getting Started
• 1.3. What Is Computer Science?
• 1.4. What Is Programming?
• 1.5. Why Study Data Structures and Abstract Data Types?
• 1.6. Why Study Algorithms?
• 1.7. Review of Basic Python
• 1.8.1. Built-in Atomic Data Types
• 1.8.2. Built-in Collection Data Types
• 1.9.1. String Formatting
• 1.10. Control Structures
• 1.11. Exception Handling
• 1.12. Defining Functions
• 1.13.1. A Fraction Class
• 1.13.2. Inheritance: Logic Gates and Circuits
• 1.14. Summary
• 1.15. Key Terms
• 1.16. Exercises
• 2.1. Objectives
• 2.2. What Is Algorithm Analysis?
• 2.3. Big O Notation
• 2.4.1. Solution 1: Anagram Detection Checking Off
• 2.4.2. Anagram Detection Solution 2: Sort and Compare
• 2.4.3. Anagram Detection Solution 3: Brute Force
• 2.4.4. Anagram Detection Solution 4: Count and Compare
• 2.5. Performance of Python Data Structures
• 2.7. Dictionaries
• 2.8. Summary
• 2.9. Key Terms
• 2.10. Exercises
• 3.1. Objectives
• 3.2. What Are Linear Structures?
• 3.3. Stacks
• 3.4. The Stack Abstract Data Type
• 3.5. Implementing a Stack in Python
• 3.6. Simple Balanced Parentheses
• 3.7. Balanced Symbols (A General Case)
• 3.8. Converting Decimal Numbers to Binary Numbers
• 3.9.1. Conversion of Infix Expressions to Prefix and Postfix
• 3.9.2. General Infix-to-Postfix Conversion
• 3.9.3. Postfix Evaluation
• 3.10. Queues
• 3.11. The Queue Abstract Data Type
• 3.12. Implementing a Queue in Python
• 3.13. Queue Simulation: Hot Potato
• 3.14.1. Main Simulation Steps
• 3.14.2. Python Implementation
• 3.14.3. Discussion
• 3.15. Deques
• 3.16. The Deque Abstract Data Type
• 3.17. Implementing a Deque in Python
• 3.18. Palindrome Checker
• 3.19. Lists
• 3.20. The Unordered List Abstract Data Type
• 3.21.1. The Node Class
• 3.21.2. The UnorderedList Class
• 3.22. The Ordered List Abstract Data Type
• 3.23.1. Analysis of Linked Lists
• 3.24. Summary
• 3.25. Key Terms
• 3.26. Exercises
• 4.1. Objectives
• 4.2. What Is Recursion?
• 4.3. Calculating the Sum of a List of Numbers
• 4.4. The Three Laws of Recursion
• 4.5. Converting an Integer to a String in Any Base
• 4.6. Stack Frames: Implementing Recursion
• 4.7. Visualizing Recursion
• 4.8. Sierpinski Triangle
• 4.9. Complex Recursive Problems
• 4.10. Tower of Hanoi
• 4.11. Exploring a Maze
• 4.12. Dynamic Programming
• 4.13. Summary
• 4.14. Key Terms
• 4.15. Exercises
• 5.1. Objectives
• 5.2. Searching
• 5.3.1. Analysis of Sequential Search
• 5.4.1. Analysis of Binary Search
• 5.5.1. Hash Functions
• 5.5.2. Collision Resolution
• 5.5.3. Implementing the Map Abstract Data Type
• 5.5.4. Analysis of Hashing
• 5.6. Sorting
• 5.7. The Bubble Sort
• 5.8. The Selection Sort
• 5.9. The Insertion Sort
• 5.10. The Shell Sort
• 5.11. The Merge Sort
• 5.12. The Quicksort
• 5.13. Summary
• 5.14. Key Terms
• 5.15. Exercises
• 6.1. Objectives
• 6.2. Examples of Trees
• 6.3. Vocabulary and Definitions
• 6.4. Implementation
• 6.5. List of Lists Representation
• 6.6. Nodes and References
• 6.7. Parse Tree
• 6.8. Tree Traversals
• 6.9. Priority Queues with Binary Heaps
• 6.10. Binary Heap Operations
• 6.11.1. The Structure Property
• 6.11.2. The Heap Order Property
• 6.11.3. Heap Operations
• 6.12. Binary Search Trees
• 6.13. Search Tree Operations
• 6.14. Search Tree Implementation
• 6.15. Search Tree Analysis
• 6.16. Balanced Binary Search Trees
• 6.17. AVL Tree Performance
• 6.18. AVL Tree Implementation
• 6.19. Summary of Map ADT Implementations
• 6.20. Summary
• 6.21. Key Terms
• 6.22. Exercises
• 7.1. Objectives
• 7.2. Vocabulary and Definitions
• 7.3. The Graph Abstract Data Type
• 7.4. An Adjacency Matrix
• 7.5. An Adjacency List
• 7.6. Implementation
• 7.7. The Word Ladder Problem
• 7.8. Building the Word Ladder Graph
• 7.9. Implementing Breadth-First Search
• 7.10. Breadth-First Search Analysis
• 7.11. The Knight’s Tour Problem
• 7.12. Building the Knight’s Tour Graph
• 7.13. Implementing Knight’s Tour
• 7.14. Knight’s Tour Analysis
• 7.15. General Depth-First Search
• 7.16. Depth-First Search Analysis
• 7.17. Topological Sorting
• 7.18. Strongly Connected Components
• 7.19. Shortest Path Problems
• 7.20. Dijkstra’s Algorithm
• 7.21. Analysis of Dijkstra’s Algorithm
• 7.22. Prim’s Spanning Tree Algorithm
• 7.23. Summary
• 7.24. Key Terms
• 7.25. Exercises
• 8.1. Objectives
• 8.2. Python Lists Revisited
• 8.3.1. Modular Arithmetic Theorems
• 8.3.2. Modular Exponentiation
• 8.3.3. The Greatest Common Divisor and Multiplicative Inverses
• 8.3.4. RSA Algorithm
• 8.4.1. The Map Abstract Data Type
• 8.4.2. Implementing a Dictionary in Python
• 8.5.1. A Quick Review of Digital Images
• 8.5.2. Quantizing an Image
• 8.5.3. An Improved Quantization Algorithm Using Octrees
• 8.6.1. Biological Strings
• 8.6.2. Simple Comparison
• 8.6.3. Using Graphs: Finite State Automata
• 8.6.4. Using Graphs: Knuth-Morris-Pratt

Acknowledgements ¶

We are very grateful to Franklin Beedle Publishers for allowing us to make this interactive textbook freely available. This online version is dedicated to the memory of our first editor, Jim Leisy, who wanted us to “change the world.”

Indices and tables ¶

Search Page

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Algorithms Every Programmer Should Know Hone your problem-solving skills by learning different algorithms and their implementation in Python

Related Papers

Camilo Vargas

alexander shen

Nakul Singh

IEEE Software

John Favaro

Enormous advances in computing power and programming environments have obscured the importance of algorithms, one of the foundational pillars of software engineering. Today, even university curricula too often pay only lip service to the teaching of algorithmic fundamentals, reinforcing the popular belief that their place at the core of a software engineer's education is past. Yet even today, the importance of algorithms in software engineering has not diminished, and the effects of neglect are evident everywhere in needlessly inefficient industrial applications. The study of algorithms must regain its rightful place of central importance in the everyday work of today's practitioner.

Diego Fernando Aguilar Mateus

In this book, Thomas Cormen, talks about most of the commonly used algorithms used in Software development

Luis Garcia

Nurullah Shahin

Marcos Porto

jyothi gandhamaneni

Python Algorithms

Mastering Basic Algorithms in the Python Language

• © 2010
• Magnus Lie Hetland

You can also search for this author in PubMed   Google Scholar

• Learn to implement classic algorithms and design new problem-solving algorithms using Python.

183k Accesses

4 Citations

4 Altmetric

This is a preview of subscription content, log in via an institution to check access.

Access this book

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (11 chapters)

Front matter, introduction, counting 101, induction and recursion… and reduction, traversal: the skeleton key of algorithmics, divide, combine, and conquer, greed is good prove it, tangled dependencies and memoization, from a to b with edsger and friends, matchings, cuts, and flows, hard problems and (limited) sloppiness, back matter.

• algorithmics
• computer science
• programming

About this book

• The book deals with some of the most important and challenging areas of programming and computer science, but in a highly pedagogic and readable manner.
• The book covers both algorithmic theory and programming practice, demonstrating how theory is reflected in real Python programs.
• Well-known algorithms and data structures that are built into the Python language are explained, and the user is shown how to implement and evaluate others himself.

About the author

Bibliographic information.

Book Title : Python Algorithms

Book Subtitle : Mastering Basic Algorithms in the Python Language

Authors : Magnus Lie Hetland

DOI : https://doi.org/10.1007/978-1-4302-3238-4

Publisher : Apress Berkeley, CA

eBook Packages : Professional and Applied Computing , Apress Access Books , Professional and Applied Computing (R0)

Copyright Information : Magnus Lie Hetland 2010

Softcover ISBN : 978-1-4302-3237-7 Published: 24 November 2010

eBook ISBN : 978-1-4302-3238-4 Published: 27 February 2011

Edition Number : 1

Number of Pages : XVI, 336

Topics : Python , Software Engineering/Programming and Operating Systems

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Navigation Menu

Search code, repositories, users, issues, pull requests..., provide feedback.

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly.

To see all available qualifiers, see our documentation .

• Notifications

A middle-to-high level open source algorithm book designed with coding interview at heart!

liyin2015/python-coding-interview

Folders and files, repository files navigation, hands-on algorithmic problem solving, a 100% open source one-stop coding interview prep book, dsa · python · leetcode · principles · problem patterns.

Contributing

The book is written in latex, and all contents except the code is located under Easy-Book folder . Feel free to send me pull requests to contribute contents. Before you start to improve the contents, it would be helpful to know the high-level structure of the book . Read How to contribute? for more details.

All contributors (with decent amount of contributions) will be listed as authors in the project.

About this book

In short, this is a middle-to-high level algorithm book designed with cracking coding interviews at hearts. It offers a one-stop coding interview prep experience. The structure of the book:

• Preparation : introduce the global picture of algorithmic problem solving and coding interviews, learn abstract data structures and highly related and useful math such as recurrence relation, and hands-on Python practice by relating the abstract data structures to Python data structures. Coding is not just code after all. ,
• Principles : we organize the design and principle here so that readers can use them as guidance while not seeking for peculiar algorithm for solving a problem.
• Classical algorithms : We enhance our algorithm database via learning how to apply the core principles to a variety of classical problems. A database that we can quickly relate to when seeing problems.
• Coding interview problem patterns : We close our book with the analyzing and categorizing problems by patterns. We address classical and best solutions for each problem pattern.

Besides trying to make the content easy to follow, here summarizes the uniqueness of this book: (1) it offers Python source code that is tailored to be simple so that it would be natural for you to use in interviews (2) all the exercises and examples are from Leetcode problems so that you get to practise online (3) Classical algorithms are explained with design principles. No algorithm is magic. (Check out advanced graph algorithms as an example) (4) problem patterns to help you tackle coding interview questions topic by topic.

How did I come up with this book?

Preparing for the coding interview is not easy! Cracking the coding interview? Nearly impossible for most of us! Luck does play a role in the outcome. So, let's just treat it as a learning process and have some fun!

Computer Science is really not just computer science. It is a combination of all fields; our normal interview problems fall into the enumerative combinatorics and our computer vision mostly consists of Linear Algebra. What really matters is our passion to learn and the ability to apply this knowledge to solve real-life problems.

There are plenty of books out there focusing on either teaching algorithmic knowledge ( Introduction to Algorithms , Algorithmic Problem Solving , etc) or introducing the interview process and solving interview problems( Cracking the Coding Interview , Coding Interview Questions , etc), but none of these books truly combine the two. This is a book designed to make up this role in the categorization. Principle, Pattern, and Leetcode Problems make up the core of this book.

This is NOT a book that provides hiring statistics for 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 algorithmic problem solving in the hope that you will be more passionate and confident about software engineering; the interview questions just set up a playground where we strengthen what we learn.

For Readers

The whole book is compiled as pdf.

For readers, you can read the book as a whole or read chapters selectively following the below links.

Table of Contents

Warm up: abstract data structures and tools.

• Abstract Data Structures
• Discrete Programming
• Recurrence Relation

Get Started: Programming and Python Data Structures

• Iteration and Recursion
• Bit Manipulation
• Python Data Structures ( source code )

Core Principles: Algorithm Design and Analysis

Complexity Analysis

Search Strategies ( source code: Graph Search , source code: Tree Traversal )

Combinatorial Search ( source code )

Reduce and Conquer

Decrease and Conquer

Binary Search, Binary Search Tree, and Segment Tree ( source code )

• Sorting and Selection ( source code: sorting algorithms , Python comparison and sort functions )
• Dynamic Programming
• Greedy Algorithms

Advanced Algorithms

• Advanced Data Structures
• Advanced Search on Linear Data Structures ( source code )
• Advanced Graph Algorithms
• String Pattern Matches
• Math and Geometry Algorithms

Problem Patterns

• 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.

Referring Books and Materials

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]

https://stanford-cs161.github.io/winter2021/schedule/

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

Mocking Interviews

Practice is important. Schedule some mocking interviews with interviewing.io . If you cant manage to register, you can join us in discord server and ask peers for practice interview.

Join me on discord server: https://discord.gg/ZXnSag7fMP , for a supportive community

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

To cite this content, please use:

Contributors 6

• Jupyter Notebook 34.9%

• Cloud Technologies
• Computer Science
• Cybersecurity
• IT Services
• Masters and Certificate Programs
• Networking and Hardware
• Operating System
• Programming
• QA and Testing
• Web Development
• Software Tools
• Career Guides
• Learn why Cybersecurity is essential
• Learn about the 4Vs of Big Data
• An Ethical Hacker's guide for getting from beginner to professional
• Data Science Basics
• Data Science for Healthcare
• Deep Learning
• Machine Learning
• Learn more about different versions of Python
• How to get started with Data Science?
• Learn about the difference between AI and ML
• What's New
• Data Analyst Salary in India
• Transportation Problem: Definition, Formulation, and Types
• Conditional Statements in Python – Python Tutorial
• How to Use Midjourney AI for Creating a Masterpiece Art?
• Business Analytics
• Business Tools
• Communication
• Entrepreneurship
• Human Resources
• Industry Programs
• Logistics and Supply Chain
• Masters and PG-Management
• Product Management
• Strategy and Leadership
• Project Management
• What are the latest trends in Recruitment?
• What are the traits required to become a successful Entrepreneur?
• Your guide to become a Business Analyst
• Henri Fayol’s 14 Principles of Management with Examples
• Top Digital Marketing Interview Questions and Answers
• Top 122 SEO Interview Questions and Answers
• Top 91 Marketing Interview Questions and Answers
• Bachelors Program
• Masters Program
• Creativity and Design
• Emerging Technologies
• Engineering-Non CS
• Energy and Environment
• Social Sciences
• Personal Development
• GitHub Copilot - About, Features and Use Cases
• Benefits of OOPs in Java
• Transformational Leadership in the Modern Era
• Digital Business: Know the Meaning and Importance
• An Overview of DEI (Diversity, Inclusion, and Inclusion)

Know the Difference Between Algorithm and Program

Algorithm is a step by step logical approach and program is a set of instructions for a given task.If you want to know more about this then read this article.

An algorithm can be defined as a systematic approach to solving a specific problem. An understanding of algorithms is essential for programmers to program more efficiently. It is equally important for programmers to understand the difference between algorithms and programs for optimizing a program according to the available resources. In this article, we will look at each term’s definitions and explore the difference between algorithm and program .

Explore the Best Programming Courses

What is an Algorithm?

An algorithm is a step-by-step and logical approach that defines a systematic process for computers to solve a specific problem. It consists of a set of rules defining how a task will be executed to get the expected results. Algorithms are conceptual and can be described using language or flowcharts. We can implement them in different programming languages​​. When we use a computer to solve a specific problem, we need to tell these steps to the solution clearly to the computer.

For example, here’s an algorithm to add two numbers:

Characteristics of an Algorithm

• An algorithm should be unambiguous.
• It should take well-defined inputs.
• The algorithm should take well-defined outputs.
• It should be simple, generic, and can be executed using the available resources.
• The algorithm must be finite.
• It must be language-independent.

Also Read: Understanding Data Structures and Algorithms in Java

Types of Algorithms

Here are the most popular type of algorithms:

• Recursive Algorithm – The Algorithm will call itself repeatedly until the problem is solved. Example: Computing factorials
• Greedy Algorithm – Algorithms that are used for solving optimization problems. Example: Huffman tree
• Brute Force Algorithm – It is one of the simplest algorithms in the concept. It iterates all possible solutions to search for one or more possible solutions that may solve a function. Example: Matching algorithm
• Backtracking Algorithm – This algorithm follows an incremental approach to finding a solution to a problem. The algorithm will solve problems recursively. It will find a solution to a problem by solving one piece of the problem at a time. If one of the solutions fails, it is removed, and the algorithm backtracks to find another solution. Example: Queens Problem
• Divide and Conquer Algorithm – As the name suggests, the algorithm is divided into two parts. The first part divides the problem into smaller subproblems of the same type. In the second part, these smaller problems are solved and then combined to produce the final solution to the problem. Example: Merge sort

Learn more about Data Structures and Algorithms

What is a Program?

A program is a set of instructions a computer follows to perform a specified task. Many programming languages can be used to write computer programs. Some popular programming languages include Python , Java , C++, JavaScript , PHP , and Ruby. These high-level programming languages are human-readable and writable. These languages are converted into low-level machine languages by compilers, interpreters, and assemblers within the computer system.

A program tells the computer how to accept input, manipulate that input, and display the output in some form that humans find useful.

For Example, in a word processor:

• The input will be the characters you type from the keyboard
• The program will format the text and correct the spelling
• Well-organized will be displayed on the screen as output

Must Read: Best Resources To Learn Programming Online

Algorithm vs Program: Difference Between Program and Algorithm

Computer algorithms solve the problem while computer programs implement them in a form that a computer can execute. Here are the main differences between algorithms and programs:

Explore Popular Data Structures and Algorithms Courses

So, this was all about the difference between algorithms and programming. We learned that the main difference is between the two is that an algorithm is a step-by-step procedure for solving the problem, while programming is a set of instructions for a computer to follow to perform a task. A program could also be an implementation of code to instruct a computer on how to execute an algorithm.

What is the main difference between an algorithm and a program?

In todayu2019s digitalized world, all the technological advancements rely on software, which is a collection of programs. Every program is built upon a series of instructions known as an algorithm. The main difference between the two is that while computer algorithms solve the problem, computer programs implement them in a form that a computer can execute.

What is a programming algorithm?

A programming algorithm defined the steps required for the computer to solve a particular problem. The computer follows the steps and shows you the results, which are called outputs. A programming algorithm is written in simple English. It has a start, a middle part, and stop part.

What are the characteristics of a good algorithm?

The characteristics of a good algorithm are Precision - each step of the algorithm must be defined clearly; Finiteness - it will stop after executing a finite number of instructions; Input - the algorithm must take an input; Output - the algorithm must produce an output.u00a0

Rashmi is a postgraduate in Biotechnology with a flair for research-oriented work and has an experience of over 13 years in content creation and social media handling. She has a diversified writing portfolio and aim... Read Full Bio

Trending Technology Courses

Top Picks & New Arrivals