A practical guide simplifying discrete math for curious minds and
demonstrating its application in solving problems related to
software development, computer algorithms, and data science Key
Features Apply the math of countable objects to practical problems
in computer science Explore modern Python libraries such as
scikit-learn, NumPy, and SciPy for performing mathematics Learn
complex statistical and mathematical concepts with the help of
hands-on examples and expert guidance Book DescriptionDiscrete
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. What you
will learn Understand the terminology and methods in discrete math
and their usage in algorithms and data problems Use Boolean algebra
in formal logic and elementary control structures Implement
combinatorics to measure computational complexity and manage memory
allocation Use random variables, calculate descriptive statistics,
and find average-case computational complexity Solve graph problems
involved in routing, pathfinding, and graph searches, such as
depth-first search Perform ML tasks such as data visualization,
regression, and dimensionality reduction Who this book is forThis
book is for computer scientists looking to expand their knowledge
of discrete math, the core topic of their field. University
students looking to get hands-on with computer science,
mathematics, statistics, engineering, or related disciplines will
also find this book useful. Basic Python programming skills and
knowledge of elementary real-number algebra are required to get
started with this book.
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