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Data science concepts and techniques...

Qamar, Usman.

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Data science concepts and techniques with applications

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data science concepts and techniques with applications/ by Usman Qamar, Muhammad Summair Raza.
Author:	Qamar, Usman.
other author:	Raza, Muhammad Summair.
Published:	Singapore :Springer Singapore : : 2020.,
Description:	xv, 196 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Applications of Data Science -- Widely used techniques in Data Science Applications -- Data Pre-processing -- Classification, Basic Concepts -- Clustering -- Text Mining -- Data Science Programming Language.
Contained By:	Springer eBooks
Subject:	Quantitative research. -
Online resource:	https://doi.org/10.1007/978-981-15-6133-7
ISBN:	9789811561337

Data science concepts and techniques with applications
Qamar, Usman.

Data science concepts and techniques with applications[electronic resource] /by Usman Qamar, Muhammad Summair Raza. - Singapore :Springer Singapore :2020. - xv, 196 p. :ill., digital ;24 cm.

Introduction -- Applications of Data Science -- Widely used techniques in Data Science Applications -- Data Pre-processing -- Classification, Basic Concepts -- Clustering -- Text Mining -- Data Science Programming Language.

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 book focuses 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.

ISBN: 9789811561337

Standard No.: 10.1007/978-981-15-6133-7doiSubjects--Topical Terms:

919734
Quantitative research.

LC Class. No.: QA76.9.Q36

Dewey Class. No.: 001.42

Data science concepts and techniques with applications
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505 0 $a Introduction -- Applications of Data Science -- Widely used techniques in Data Science Applications -- Data Pre-processing -- Classification, Basic Concepts -- Clustering -- Text Mining -- Data Science Programming Language.
520 $a 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 book focuses 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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