DATA SCIENCE HANDBOOK
This desk reference handbook gives a hands-on experience on various algorithms and popular techniques used in real-time in data science to all researchers working in various domains.
Data Science is one of the leading research-driven areas in the modern era. It is having a critical role in healthcare, engineering, education, mechatronics, and medical robotics. Building models and working with data is not value-neutral. We choose the problems with which we work, make assumptions in these models, and decide on metrics and algorithms for the problems. The data scientist identifies the problem which can be solved with data and expert tools of modeling and coding.
The book starts with introductory concepts in data science like data munging, data preparation, and transforming data. Chapter 2 discusses data visualization, drawing various plots and histograms. Chapter 3 covers mathematics and statistics for data science. Chapter 4 mainly focuses on machine learning algorithms in data science. Chapter 5 comprises of outlier analysis and DBSCAN algorithm. Chapter 6 focuses on clustering. Chapter 7 discusses network analysis. Chapter 8 mainly focuses on regression and naive-bayes classifier. Chapter 9 covers web-based data visualizations with Plotly. Chapter 10 discusses web scraping.
The book concludes with a section discussing 19 projects on various subjects in data science.
Audience
The handbook will be used by graduate students up to research scholars in computer science and electrical engineering as well as industry professionals in a range of industries such as healthcare.
This desk reference handbook gives a hands-on experience on various algorithms and popular techniques used in real-time in data science to all researchers working in various domains.
Data Science is one of the leading research-driven areas in the modern era. It is having a critical role in healthcare, engineering, education, mechatronics, and medical robotics. Building models and working with data is not value-neutral. We choose the problems with which we work, make assumptions in these models, and decide on metrics and algorithms for the problems. The data scientist identifies the problem which can be solved with data and expert tools of modeling and coding.
The book starts with introductory concepts in data science like data munging, data preparation, and transforming data. Chapter 2 discusses data visualization, drawing various plots and histograms. Chapter 3 covers mathematics and statistics for data science. Chapter 4 mainly focuses on machine learning algorithms in data science. Chapter 5 comprises of outlier analysis and DBSCAN algorithm. Chapter 6 focuses on clustering. Chapter 7 discusses network analysis. Chapter 8 mainly focuses on regression and naive-bayes classifier. Chapter 9 covers web-based data visualizations with Plotly. Chapter 10 discusses web scraping.
The book concludes with a section discussing 19 projects on various subjects in data science.
Audience
The handbook will be used by graduate students up to research scholars in computer science and electrical engineering as well as industry professionals in a range of industries such as healthcare.
€134.99
About the author
Kolla Bhanu Prakash, Ph D, is a Professor and Research Group Head for A.I. & Data Science Research group at K L University, India. He has published more than 80 research papers in international and national journals and conferences, as well as authored/edited 12 books and seven patents. His research interests include deep learning, data science, and quantum computing.Buy this ebook and get 1 more FREE!
Language English ● Format EPUB ● Pages 480 ● ISBN 9781119857990 ● File size 48.3 MB ● Editor Kolla Bhanu Prakash ● Publisher John Wiley & Sons ● Published 2022 ● Edition 1 ● Downloadable 24 months ● Currency EUR ● ID 8681022 ● Copy protection Adobe DRM
Requires a DRM capable ebook reader
More ebooks from the same author(s) / Editor
16,016 Ebooks in this category
