Best Data Structure and Algorithms Books

In data science, algorithms and data structures make up the function and storage of data collecting.

1. Cracking the Coding Interview: 189 Programming Questions and Solutions

Author: by Gayle Laakmann McDowell
Published at: CareerCup; 6th edition (July 1, 2015)
ISBN: 978-0984782857

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I am not a recruiter. I am a software engineer. And as such, I know what it’s like to be asked to whip up brilliant algorithms on the spot and then write flawless code on a whiteboard. I’ve been through this as a candidate and as an interviewer.

Cracking the Coding Interview, 6th Edition is here to help you through this process, teaching you what you need to know and enabling you to perform at your very best. I’ve coached and interviewed hundreds of software engineers. The result is this book.

Learn how to uncover the hints and hidden details in a question, discover how to break down a problem into manageable chunks, develop techniques to unstick yourself when stuck, learn (or re-learn) core computer science concepts, and practice on 189 interview questions and solutions.

These interview questions are real; they are not pulled out of computer science textbooks. They reflect what’s truly being asked at the top companies, so that you can be as prepared as possible.WHAT’S INSIDE? 189 programming interview questions, ranging from the basics to the trickiest algorithm problems.

2. Introduction to Algorithms, 3rd Edition (The MIT Press)

Author: by Thomas H. Cormen
Published at: MIT Press; 3rd edition (September 1, 2009)
ISBN: 978-0262033848

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The latest edition of the essential text and professional reference, with substantial new material on such topics as vEB trees, multithreaded algorithms, dynamic programming, and edge-based flow. Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor.

Introduction to Algorithms uniquely combines rigor and comprehensiveness. The book covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of study.

The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor. The first edition became a widely used text in universities worldwide as well as the standard reference for professionals.

The second edition featured new chapters on the role of algorithms, probabilistic analysis and randomized algorithms, and linear programming. The third edition has been revised and updated throughout. It includes two completely new chapters, on van Emde Boas trees and multithreaded algorithms, substantial additions to the chapter on recurrence (now called Divide-and-Conquer), and an appendix on matrices.

3. A Programmer's Guide to Computer Science: A virtual degree for the self-taught developer

Author: by Dr. William M Springer II
Published at: Jaxson Media; Illustrated edition (July 28, 2019)
ISBN: 978-1951204006

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You know how to code……But is it enough? Do you feel left out when other programmers talk about asymptotic bounds? Have you failed a job interview because you don’t know computer science? Volume one covers the most frequently referenced topics, including: – Algorithms and data structures – Graphs – Problem-solving techniques – Complexity theory The author, a senior developer at a major software company with a PhD in computer science, takes you through what you would have learned while earning a four-year computer science degree.

When you finish this book, you’ll have the tools you need to hold your own with people who have – or expect you to have – a computer science degree. Don’t let the lack of a degree hold back your career.

Dive into this book now.

4. Problem Solving with Algorithms and Data Structures Using Python SECOND EDITION

Author: by Bradley N. Miller
Published at: Franklin, Beedle & Associates; 2nd edition (August 22, 2011)
ISBN: 978-1590282571

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THIS TEXTBOOK is about computer science. It is also about Python. However, there is much more. The study of algorithms and data structures is central to understanding what computer science is all about. Learning computer science is not unlike learning any other type of difficult subject matter.

The only way to be successful is through deliberate and incremental exposure to the fundamental ideas. A beginning computer scientist needs practice so that there is a thorough understanding before continuing on to the more complex parts of the curriculum.

In addition, a beginner needs to be given the opportunity to be successful and gain confidence. This textbook is designed to serve as a text for a first course on data structures and algorithms, typically taught as the second course in the computer science curriculum.

Even though the second course is considered more advanced than the first course, this book assumes you are beginners at this level. You may still be struggling with some of the basic ideas and skills from a first computer science course and yet be ready to further explore the discipline and continue to practice problem solving.

5. Data Structures and Algorithms in Python

Author: by Michael T. Goodrich
Published at: Wiley; 1st edition (March 18, 2013)
ISBN: 978-1118290279

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Based on the authors’ market leading data structures books in Java and C++, this textbook offers a comprehensive, definitive introduction to data structures in Python by respected authors. Data Structures and Algorithms in Python is the first mainstream object-oriented book available for the Python data structures course.

Designed to provide a comprehensive introduction to data structures and algorithms, including their design, analysis, and implementation, the text will maintain the same general structure as Data Structures and Algorithms in Java and Data Structures and Algorithms in C++.

6. Data Structures and Algorithms in Java

Author: by Robert Lafore
Published at: Sams Publishing; 2nd edition (November 6, 2002)
ISBN: 978-0672324536

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Data Structures and Algorithms in Java, Second Edition is designed to be easy to read and understand although the topic itself is complicated. Algorithms are the procedures that software programs use to manipulate data structures. Besides clear and simple example programs, the author includes a workshop as a small demonstration program executable on a Web browser.

The programs demonstrate in graphical form what data structures look like and how they operate. In the second edition, the program is rewritten to improve operation and clarify the algorithms, the example programs are revised to work with the latest version of the Java JDK, and questions and exercises will be added at the end of each chapter making the book even more useful.

Educational Supplement Suggested solutions to the programming projects found at the end of each chapter are made available to instructors at recognized educational institutions. This educational supplement can be found at www.Prenhall. Com, in the Instructor Resource Center.

7. Ultimate Step by Step Guide to Machine Learning Using Python: Predictive modelling concepts explained in simple terms for beginners

Author: by Daneyal Anis
Published at: Independently published (February 17, 2020)
ISBN: 979-8611399538

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Start your Data Science career using Python today! Are you ready to start your new exciting career? Ready to crush your machine learning career goals? Are you overwhelmed with complexity of the books on this subject? Then let this breezy and fun little book on Python and machine learning models make you a data scientist in 7 days!

First part of this book introduces Python basics including:1) Data Structures like Pandas 2) Foundational libraries like Numpy, Seaborn and Scikit-Learn Second part of this book shows you how to build predictive machine learning models step by step using techniques such as: 1) Regression analysis 2) Decision tree analysis 3) Training and testing data models 4) And much more!

After reading this book you will be able to: 1) Code in Python with confidence 2) Build new machine learning models from scratch 3) Know how to clean and prepare your data for analytics 4) Speak confidently about statistical analysis techniques Data Science was ranked the fast-growing field by LinkedIn and Data Scientist is one of the most highly sought after and lucrative careers in the world!

8. Data Structures and Other Objects Using C++

Author: by Michael Main
Published at: Pearson; 4th edition (February 24, 2010)
ISBN: 978-0132129480

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This book successfully balances the introduction of object-oriented concepts with data structures in C++. KEY TOPICS: Provides interfaces for the principal example classes, which are compliant with the ANSI/ISO C++ Standard Library classes. Thorough coverage of the role of the const keyword in the C++ Standard Library.

Covers C++ features such as namespaces, static member constants, typename keyword, and inheritance. Thorough review of C++ syntax and OOP concepts, make the book accessible for programmers at various levels. The book also gives readers a firm grasp of key concepts and allows programmers experienced in another language to adjust easily.

A solid foundation in building and using abstract data types is also provided, along with an assortment of advanced topics such as B-trees for project building and graphs. MARKET: This book is designed for novice programmers who have learned the concepts of objects and classes and want to move on to the data structures topics of recursion and data abstraction.

9. Data Structures and Algorithms

Author: by Alfred Aho
Published at: Pearson; 1st edition (January 1, 1983)
ISBN: 978-0201000238

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The authors’ treatment of data structures in Data Structures and Algorithms is unified by an informal notion of “abstract data types,” allowing readers to compare different implementations of the same concept. Algorithm design techniques are also stressed and basic algorithm analysis is covered.

Most of the programs are written in Pascal.

10. C++ Programming: Program Design Including Data Structures

Author: by D. S. Malik
Published at: Cengage Learning; 7th edition (March 26, 2014)
ISBN: 978-1285852751

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C++ PROGRAMMING: PROGRAM DESIGN INCLUDING DATA STRUCTURES, Sixth Edition remains the definitive text for the CS1/CS2 course sequence.D.S. Malik’s time-tested, student-centered methodology uses a strong focus on problem-solving and full-code examples to vividly demonstrate the how and why of applying programming concepts and utilizing C++ to work through a problem.

This new edition includes updated end-of-chapter exercises, new debugging exercises, an earlier introduction to variables and a streamlined discussion of user-discussion of user-defined functions. Malik’s text ensures students learn how to apply the C++ programming language, and are motivated to understand the why?

Behind key C++ concepts.

11. Data Structures with C++ Using STL

Author: by William Ford
Published at: Pearson; 2nd edition (July 17, 2001)
ISBN: 978-0130858504

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This book uses a modern object-oriented approach to data structures, unified around the notion of the Standard Template Library (STL) container classes. The book presents a systematic development of data structures supported by numerous examples and complete programs. The authors separate the applications of a data structure from its implementation.

KEY TOPICS: Includes an applied study of interesting and classical algorithms that illustrate the data structures using only simple mathematical concepts (Big-O notation is introduced intuitively); Many additional figures are integrated into the presentation; ADT (Abstract Data Type) for each data structureimmediately used to solve appropriate problems; Early and accessible introduction to templates and iterators; Use of modern C++ constructs in developing data structures and their applications provides enough language detail to sufficiently understand the constructs.MARKET:

12. Data Structures, Algorithms, and Software Principles in C

Author: by Thomas Standish
Published at: Pearson; 1st edition (September 30, 1994)
ISBN: 978-0201591187

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Using C, this book develops the concepts and theory of data structures and algorithm analysis in a gradual, step-by-step manner, proceeding from concrete examples to abstract principles. Standish covers a wide range of both traditional and contemporary software engineering topics.

The text also includes an introduction to object-oriented programming using C++. By introducing recurring themes such as levels of abstraction, recursion, efficiency, representation and trade-offs, the author unifies the material throughout. Mathematical foundations can be incorporated at a variety of depths, allowing the appropriate amount of math for each user.

13. Data Structures and Other Objects Using Java

Author: by Michael Main
Published at: Pearson; 4th edition (November 2, 2011)
ISBN: 978-0132576246

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Data Structures and Other Objects Using Java is a gradual, “just-in-time” introduction to Data Structures for a CS2 course. Each chapter provides a review of the key aspects of object-oriented programming and a syntax review, giving students the foundation for understanding significant programming concepts.

With this framework they are able to accomplish writing functional data structures by using a five-step method for working with data types; understanding the data type abstractly, writing a specification, using the data type, designing and implementing the data type, and analyzing the implementation.

Students learn to think analytically about the efficiency and efficacy of design while gaining exposure to useful Java classes libraries.

14. The Garbage Collection Handbook: The Art of Automatic Memory Management (Chapman & Hall/CRC Applied Algorithms and Data Structures series)

Author: by Richard Jones
Published at: Chapman and Hall/CRC; 1st edition (August 17, 2011)
ISBN: 978-1420082791

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Published in 1996, Richard Jones’s Garbage Collection was a milestone in the area of automatic memory management. The field has grown considerably since then, sparking a need for an updated look at the latest state-of-the-art developments. The Garbage Collection Handbook: The Art of Automatic Memory Management brings together a wealth of knowledge gathered by automatic memory management researchers and developers over the past fifty years.

The authors compare the most important approaches and state-of-the-art techniques in a single, accessible framework. The book addresses new challenges to garbage collection made by recent advances in hardware and software. It explores the consequences of these changes for designers and implementers of high performance garbage collectors.

Along with simple and traditional algorithms, the book covers parallel, incremental, concurrent, and real-time garbage collection. Algorithms and concepts are often described with pseudocode and illustrations. The nearly universal adoption of garbage collection by modern programming languages makes a thorough understanding of this topic essential for any programmer.

15. Algorithms + Data Structures = Programs (Prentice-Hall Series in Automatic Computation)

Author: by Niklaus Wirth
Published at: Prentice Hall; 1st edition (February 1, 1976)
ISBN: 978-0130224187

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16. Data Model Patterns: Conventions of Thought

Author: by David C. Hay
Published at: Dorset House (January 1, 1996)
ISBN: 978-0932633293

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Learning the basics of a modeling technique is not the same as learning how to use and apply it. To develop a data model of an organization is to gain insights into its nature that do not come easily. Indeed, analysts are often expected to understand subtleties of an organization’s structure that may have evaded people who have worked there for years.

Here’s help for those analysts who have learned the basics of data modeling (or “entity/relationship modeling”) but who need to obtain the insights required to prepare a good model of a real business. Structures common to many types of business are analyzed in areas such as accounting, material requirements planning, process manufacturing, contracts, laboratories, and documents.

Topics In each chapter, high-level data models are drawn from the following business areas: * The Enterprise and Its World * The Things of the Enterprise * Procedures and Activities * Contracts * Accounting * The Laboratory * Material Requirements Planning * Process Manufacturing * Documents * Lower-Level Conventions