Manisha S. Bhende

Fundamentals of Data Science is designed for students, academicians and practitioners with a complete walkthrough right from the foundational groundwork required to outlining all the concepts, techniques and tools required to understand Data Science. Data Science is an umbrella term for the non-traditional techniques and technologies that are required to collect, aggregate, process, and gain insights from massive datasets. This book offers all the processes, methodologies, various steps like data acquisition, pre-process, mining, prediction, and visualization tools for extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processesReaders will learn the steps necessary to create the application with SQl, NoSQL, Python, R, Matlab, Octave and Tablue. This book provides a stepwise approach to building solutions to data science applications right from understanding the fundamentals, performing data analytics to writing source code. All the concepts are discussed in simple English to help the community to become Data Scientist without much pre-requisite knowledge. Features : Simple strategies for developing statistical models that analyze data and detect patterns, trends, and relationships in data sets. Complete roadmap to Data Science approach with dedicatedsections which includes Fundamentals, Methodology and Tools. Focussed approach for learning and practice various Data Science Toolswith Sample code and examples for practice. Information is presented in an accessible way for students, researchers and academicians and professionals.

Fundamentals of Data Science is designed for students, academicians and practitioners with a complete walkthrough right from the foundational groundwork required to outlining all the concepts, techniques and tools required to understand Data Science. Data Science is an umbrella term for the non-traditional techniques and technologies that are required to collect, aggregate, process, and gain insights from massive datasets. This book offers all the processes, methodologies, various steps like data acquisition, pre-process, mining, prediction, and visualization tools for extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processesReaders will learn the steps necessary to create the application with SQl, NoSQL, Python, R, Matlab, Octave and Tablue. This book provides a stepwise approach to building solutions to data science applications right from understanding the fundamentals, performing data analytics to writing source code. All the concepts are discussed in simple English to help the community to become Data Scientist without much pre-requisite knowledge. Features : Simple strategies for developing statistical models that analyze data and detect patterns, trends, and relationships in data sets. Complete roadmap to Data Science approach with dedicatedsections which includes Fundamentals, Methodology and Tools. Focussed approach for learning and practice various Data Science Toolswith Sample code and examples for practice. Information is presented in an accessible way for students, researchers and academicians and professionals.

This book explores the field of polymer science and engineering with coverage from foundational principles to cutting-edge advancements. It provides ample coverage of polymer characterization techniques, including spectroscopy, chromatography, microscopy, and thermal analysis. The book also delves into practical aspects of polymer processing and manufacturing, exploring techniques such as extrusion, injection molding, and 3D printing. Examples and case studies illustrate the practical relevance of polymer science, from biomedical implants to sustainable packaging solutions are included. Key features- -Combines foundational aspects of polymers with contemporary advancements, especially in areas like living polymerizations -Covers a wide range of industries, including automotive, aerospace, healthcare, electronics, packaging, and construction, with specific examples and applications in each sector. -Discusses computational techniques, including AI and machine learning applications, for optimizing polymer design and performance -Illustrates sustainable polymer production, recycling strategies, and the development of biodegradable polymers -Includes case studies and real-world applications from globally recognized companies such as BASF, Dow Chemical, and Toray Industries This book is aimed at researchers and graduate students in polymer and materials engineering.