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Data-Intensive Computing: Architectures, Algorithms, and Applications

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The world is awash with digital data from social networks, blogs, business, science, and engineering. Data-intensive computing facilitates understanding of complex problems that must process massive amounts of data. Through the development of new classes of software, algorithms, and hardware, data-intensive applications can provide timely and meaningful analytical results in response to exponentially growing data complexity and associated analysis requirements. This emerging area brings many challenges that are different from traditional high-performance computing. This reference for computing professionals and researchers describes the dimensions of the field, the key challenges, the state of the art, and the characteristics of likely approaches that future data-intensive problems will require. Chapters cover general principles and methods for designing such systems and for managing and analyzing the big data sets of today that live in the cloud, and describe example applications in bioinformatics and cybersecurity that illustrate these principles in practice.

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Introducing Data Science: Big Data, Machine Learning, and more, using Python tools
Introducing Data Science: Big Data, Machine Learning, and more, using Python tools

Summary

Introducing Data Science teaches you how to accomplish the fundamental tasks that occupy data scientists. Using the Python language and common Python libraries, you'll experience firsthand the challenges of dealing with data at scale and gain a solid foundation in data science.

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Beginning Software Engineering
Beginning Software Engineering

A complete introduction to building robust and reliable software

Beginning Software Engineering demystifies the software engineering methodologies and techniques that professional developers use to design and build robust, efficient, and consistently reliable software. Free of jargon and assuming no previous
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Making Sense of Data: Designing Effective Visualizations
Making Sense of Data: Designing Effective Visualizations

You have a mound of data sitting in front of you and a suite of computation tools at your disposal. And yet, you’re stumped as to how to turn that data into insight. Which part of that data actually matters, and where is this insight hidden?

If you’re a data scientist who struggles to navigate the murky space...


Writing for Science and Engineering, Second Edition: Papers, Presentations and Reports (Elsevier Insights)
Writing for Science and Engineering, Second Edition: Papers, Presentations and Reports (Elsevier Insights)

Learning how to write clearly and concisely is an integral part of furthering your research career; however, doing so is not always easy. In this second edition, fully updated and revised, Dr. Silyn-Roberts explains in plain English the steps to writing abstracts, theses, journal papers, funding bids, literature reviews, and more. The book...

Probability Theory: A Comprehensive Course (Universitext)
Probability Theory: A Comprehensive Course (Universitext)

This second edition of the popular textbook contains a comprehensive course in modern probability theory. Overall, probabilistic concepts play an increasingly important role in mathematics, physics, biology, financial engineering and computer science. They help us in understanding magnetism, amorphous media, genetic diversity and the perils...

Program Management Leadership: Creating Successful Team Dynamics (Best Practices and Advances in Program Management)
Program Management Leadership: Creating Successful Team Dynamics (Best Practices and Advances in Program Management)

This is not another how-to guide for program managers or another reiteration of the Project Management Institute’s standards for program management. Instead, Program Management Leadership: Creating Successful Team Dynamics examines various leadership approaches and illustrates the value of effective leadership styles in...

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