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Practical Discrete Mathematics

Practical Discrete Mathematics

By : Ryan T. White, Ray
4.6 (17)
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Practical Discrete Mathematics

Practical Discrete Mathematics

4.6 (17)
By: Ryan T. White, Ray

Overview of this book

Discrete mathematics deals with studying countable, distinct elements, and its principles are widely used in building algorithms for computer science and data science. The knowledge of discrete math concepts will help you understand the algorithms, binary, and general mathematics that sit at the core of data-driven tasks. Practical Discrete Mathematics is a comprehensive introduction for those who are new to the mathematics of countable objects. This book will help you get up to speed with using discrete math principles to take your computer science skills to a more advanced level. As you learn the language of discrete mathematics, you’ll also cover methods crucial to studying and describing computer science and machine learning objects and algorithms. The chapters that follow will guide you through how memory and CPUs work. In addition to this, you’ll understand how to analyze data for useful patterns, before finally exploring how to apply math concepts in network routing, web searching, and data science. By the end of this book, you’ll have a deeper understanding of discrete math and its applications in computer science, and be ready to work on real-world algorithm development and machine learning.
Table of Contents (17 chapters)
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1
Part I – Basic Concepts of Discrete Math
7
Part II – Implementing Discrete Mathematics in Data and Computer Science
12
Part III – Real-World Applications of Discrete Mathematics

Computational complexity of algorithms

In this section, we will learn about what algorithms are, the complexity of algorithms, and what they mean in terms of time and space and Big-O notation (compact notation for classifying the time and space needed for an algorithm). By the end of this section, you should have a good understanding of what algorithms are and their characteristics, such as complexity, and be able to determine the Big-O notation for the complexity of algorithms.

Algorithms are a step-by-step procedure/instruction to solve a problem or to obtain a desired output. They can be implemented in any programming language. Some of the important categories of algorithms from a data structure point of view are as follows:

  • Search: Used to search for an item in a data structure
  • Sort: Used to sort items in a required order
  • Insert: Used to insert items into a data structure
  • Update: Used to update an existing item in a data structure
  • Delete: Used...

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