"data Analytics"

Deep dive SQL Server and data analytics queries. Followed by using Tableau to visualise the outputs from the queries

This book presents the proceedings of the 2020 2nd International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT-2021), online conference, on 30 October 2021. It provides comprehensive coverage of the latest advances and trends in information technology, science and engineering, addressing a number of broad themes, including novel machine learning and big data analytics methods for IoT security, data mining and statistical modelling for the secure IoT and machine learning-based security detecting protocols, which inspire the development of IoT security and privacy technologies.

This book constitutes refereed proceedings of the International Conference on Security, Privacy and Data Analytics, ISPDA 2021. The volume covers a wide range of topics, including big data and analytics, cloud security and privacy, data intelligence, hardware security, network security, blockchain technology and distributed ledger, machine learning for security, and many others. The volume includes novel contributions and the latest developments from researchers across industry and academia working in security, privacy, and data analytics from technological and social perspectives. The book will serve as a valuable reference resource for academics and researchers across the globe.

Data Science for Infectious Disease Data Analytics: An Introduction with R provides an overview of modern data science tools and methods that have been developed specifically to analyze infectious disease data. With a quick start guide to epidemiological data visualization and analysis in R, this book spans the gulf between academia and practices providing many lively, instructive data analysis examples using the most up-to-date data, such as the newly discovered coronavirus disease (COVID-19).

This book is a comprehensive introduction to the methods and algorithms of modern data analytics. It provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications. This book has been used for more than ten years in the Data Mining course at the Technical University of Munich. Much of the content is based on the results of industrial research and development projects at Siemens.