Learn Data Structure and Algorithmic Thinking with Python for Beginners

This book is about the essentials of data structures and algorithms: the basic elements from which large and complex software projects are built.

To grow a good understanding or a data structure necessitates three things: first, you must learn how the information is organised in the memory of the computer; second, you must become acquainted with the algorithms to manipulate the information confined in the data structure; and third, you must understand the performance characteristics or the data structure so that when called upon to select a suitable data structure for a particular application, you are able to make an appropriate decision. The algorithms and data structures in this book are presented in the Python programming language. A unique feature of this book, when compared to the available books on the subject, is that it offers a balance of theory, practical concepts, problem solving, interview questions, and much more.

This book contains salient features in which Basic principles of algorithm design is described in detail. In addition, the representation of well-known data structures in Python, the implementation of well-known algorithms in Python and methods to transform new problems into well-known algorithmic problems with efficient solutions. Furthermore, the procedure to analyze algorithms and Python programs using both mathematical tools and basic experiments; benchmarks is also explained in this book. Lastly, way to understand several classical algorithms and data structures in depth, and be able to deploy these efficiently in Python.

Some key terms that will be discussed in this book are given below:

  • Variables
  • Data types
  • Data Structures
  • Abstract Data Types {ADTs)
  • What is an Algorithm?
  • Why the Analysis of Algorithms?
  • Goal or the Analysis or Algorithms
  • What is Running Time Analysis?
  • How to Compare Algorithms?
  • What is Rate or Growth?
  • Commonly Used Rates or Growth
  • Types of Analysis
  • Asymptotic Notation
  • Big-0 Notation
  • Omega-n Notation
  • Thela-0 Notation
  • Why is it called Asymptotic Analysis?
  • Guidelines for Asymptotic Analysis
  • Properties or Notations
  • Commonly used Logarithms and Summations
  • Master Theorem for Divide and Conquer
  • Divide and Conquer Master Theorem: Problems & Solutions
  • Master Theorem for Subtract and Conquer Recurrences
  • Variant or Subtraction and Conquer Master Theorem
  • Method of Guessing and Confirming
  • Amortized Analysis
  • Algorithms Analysis: Problems &. Solutions
  • What is Recursion?
  • Why Recursion?
  • Format or a Recursive Function
  • Recursion and Memory (Visualization)
  • Recursion versus Iteration
  • Notes on Recursion
  • Example Algorithms of Recursion
  • Recursion: Problems & Solutions
  • What is Buck tracking?
  • Example Algorithms of Backtracking
  • Backtracking: Problems & Solutions

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