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Data Science Concepts and Techniques with Applications
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Data Science Concepts and Techniques with Applications

2020 ed.

Book Details

Format Paperback / Softback
ISBN-10 9811561354
ISBN-13 9789811561351
Edition 2020 ed.
Publisher Springer Verlag, Singapore
Imprint Springer Verlag, Singapore
Country of Manufacture GB
Country of Publication GB
Publication Date Jun 9th, 2021
Print length 196 Pages
Weight 374 grams
Dimensions 15.40 x 23.50 x 1.30 cms
Ksh 8,100.00
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Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. Followed by discussion on wide range of applications of data science and widely used techniques in data science.The second section is devoted to the tools and techniques of data science.
This book comprehensively covers the topic of data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three sections:The first section is an introduction to data science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics. Followed by discussion on wide range of applications of data science and widely used techniques in data science. The second section is devoted to the tools and techniques of data science. It consists of data pre-processing, feature selection, classification and clustering concepts as well as an introduction to text mining and opining mining. And finally, the third section of the bookfocuses on two programming languages commonly used for data science projects i.e. Python and R programming language. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. The book is suitable for both undergraduate and postgraduate students as well as those carrying out research in data science. It can be used as a textbook for undergraduate students in computer science, engineering and mathematics. It can also be accessible to undergraduate students from other areas with the adequate background. The more advanced chapters can be used by postgraduate researchers intending to gather a deeper theoretical understanding.

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