Open access peer-reviewed Edited Volume

Introduction to Data Science and Machine Learning

Edited by Keshav Sud

University of Illinois at Chicago


Pakize Erdogmus

Duzce University

Seifedine Kadry

Beirut Arab University

“Introduction to Data Science and Machine Learning” has been created with the goal to provide beginners seeking to learn about data science, data enthusiasts, and experienced data professionals with a deep understanding of data science application development using open-source programming from start to finish. This book is divided into four sections: the first section contains an introduction to the book, the second covers the field of data science, software development, and open-source based embedded hardware; the third section covers algorithms that are the decision engines for data science applications; and the final section brings together the concepts shared in the first three sections and provides several examples of data science applications.

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Introduction to Data Science and Machine LearningEdited by Keshav Sud

Published: March 25th 2020

DOI: 10.5772/intechopen.77469

ISBN: 978-1-83880-334-6

Print ISBN: 978-1-83880-333-9

eBook (PDF) ISBN: 978-1-83880-371-1

Copyright year: 2020

Books open for chapter submissions

3787 Total Chapter Downloads

1 Crossref Citations

2 Dimensions Citations


Open access peer-reviewed

1. Introductory Chapter: Clustering with Nature-Inspired Optimization Algorithms

By Pakize Erdogmus and Fatih Kayaalp


Open access peer-reviewed

2. Best Practices in Accelerating the Data Science Process in Python

By Deanne Larson


Open access peer-reviewed

3. Software Design for Success

By Laura M. Castro


Open access peer-reviewed

4. Embedded Systems Based on Open Source Platforms

By Zlatko Bundalo and Dusanka Bundalo


Open access peer-reviewed

5. The K-Means Algorithm Evolution

By Joaquín Pérez-Ortega, Nelva Nely Almanza-Ortega, Andrea Vega-Villalobos, Rodolfo Pazos-Rangel, Crispín Zavala-Díaz and Alicia Martínez-Rebollar


Open access peer-reviewed

6. “Set of Strings” Framework for Big Data Modeling

By Igor Sheremet


Open access peer-reviewed

7. Investigation of Fuzzy Inductive Modeling Method in Forecasting Problems

By Yu. Zaychenko and Helen Zaychenko


Open access peer-reviewed

8. Segmenting Images Using Hybridization of K-Means and Fuzzy C-Means Algorithms

By Raja Kishor Duggirala


Open access peer-reviewed

9. The Software to the Soft Target Assessment

By Lucia Mrazkova Duricova, Martin Hromada and Jan Mrazek


Open access peer-reviewed

10. The Methodological Standard to the Assessment of the Traffic Simulation in Real Time

By Jan Mrazek, Martin Hromada and Lucia Duricova Mrazkova


Open access peer-reviewed

11. Augmented Post Systems: Syntax, Semantics, and Applications

By Igor Sheremet


Open access peer-reviewed

12. Serialization in Object-Oriented Programming Languages

By Konrad Grochowski, Michał Breiter and Robert Nowak


Edited Volume and chapters are indexed in

  • Worldcat
  • OpenAIRE
  • Google Scholar
  • AZ ebsco
  • Base
  • CNKI

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