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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

Summary

In this chapter, we learned about computer algorithms, and their complexities (time and space). We also discussed how these complexities vary based on the size of the input. We investigated the different types of time complexities, including constant, linear, quadratic, cubic, and exponential, along with their Big-O notations. We then looked into the complexities of fundamental control structures and discussed these with regard to three fundamental flow types – sequential, selection, and repetitive flow. The complexities of linear and binary search algorithms were discussed in addition to the best-, worst-, and average-case scenarios. Toward the end, we learned about some other kinds of time complexity types, such as P and NP.

With the knowledge acquired in this chapter, you will be well equipped to choose the right kind of algorithm to solve a certain problem. In the next chapter, we will be looking into terminology and notation for trees, graphs, and networks...

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