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A hands-on guide for professionals to perform various data science tasks in R Key Features Explore the popular R packages for data science Use R for efficient data mining, text analytics and feature engineering Become a thorough data science professional with the help of hands-on examples and use-cases in R Book Description R is the most widely used programming language, and when used in association with data science, this powerful combination will solve the complexities involved with unstructured datasets in the real world. This book covers the entire data science ecosystem for aspiring data scientists, right from zero to a level where you are confident enough to get hands-on with real-world data science problems. The book starts with an introduction to data science and introduces readers to popular R libraries for executing data science routine tasks. This book covers all the important processes in data science such as data gathering, cleaning data, and then uncovering patterns from it. You will explore algorithms such as machine learning algorithms, predictive analytical models, and finally deep learning algorithms. You will learn to run the most powerful visualization packages available in R so as to ensure that you can easily derive insights from your data. Towards the end, you will also learn how to integrate R with Spark and Hadoop and perform large-scale data analytics without much complexity. What you will learn Understand the R programming language and its ecosystem of packages for data science Obtain and clean your data before processing Master essential exploratory techniques for summarizing data Examine various machine learning prediction, models Explore the H2O analytics platform in R for deep learning Apply data mining techniques to available datasets Work with interactive visualization packages in R Integrate R with Spark and Hadoop for large-scale data analytics Who this book is for If you are a budding data scientist keen to learn about the popular pandas library, or a Python developer looking to step into the world of data analysis, this book is the ideal resource you need to get started. Some programming experience in Python will be helpful to get the most out of this course

Software. --- Data Science.

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Big Data. --- Data Science.

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In recent years, popular media have inundated audiences with sensationalised headlines recounting data breaches, new forms of surveillance and other dangers of our digital age. Despite their regularity, such accounts treat each case as unprecedented and unique. This book proposes a radical rethinking of the history, present and future of our relations with the digital, spatial technologies that increasingly mediate our everyday lives. From smartphones to surveillance cameras, to navigational satellites, these new technologies offer visions of integrated, smooth and efficient societies, even as they directly conflict with the ways users experience them. Recognising the potential for both control and liberation, the authors argue against both acquiescence to and rejection of these technologies. Through intentional use of the very systems that monitor them, activists from Charlottesville to Hong Kong are subverting, resisting and repurposing geographic technologies. Using examples as varied as writings on the first telephones to the experiences of a feminist collective for migrant women in Spain, the authors present a revolution of everyday technologies. In the face of the seemingly inevitable dominance of corporate interests, these technologies allow us to create new spaces of affinity, and a new politics of change.

Computers / Data Science --- Computers

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The European Data Protection Regulation applies since May 25th, 2018. It creates a uniform data protection legal framework within the EU. National and international medical research projects, regardless of whether they were started before or after the introduction of the GDPR, are obliged to follow this new regulation and implement it promptly. This raises various challenges for a large number of medical research projects. The University Medicine Greifswald commissioned this legal report, that was prepared by DIERKS+COMPANY. Two real-world research projects, the Baltic Fracture Competence Centre (BFCC) as well as the German Centre for Cardiovascular Research (DZHK) provide use cases, questions, and context for this legal report. It addresses questions regarding all steps of data processing. The report provides practical answers to a wide array of technical and organisational questions in the area of data protection-compliant processing of research data. A comprehensive guide to GDPR-compliant data processing has been developed, which both summarises the broad legal environment and provides specific assistance in the design and implementation of GDPR-compliant data management processes, including Informed Consent, Legal Consequences of Withdrawal, and Privacy by Design.

Computers / Data Science --- Computers --- Computers.

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Information systems --- data processing --- data science