Strand: New Concepts in Parallel Programming

Computers & Technology

Taylor, Stephen

Foster, Ian

Programming Languages

PRENTICE/HALL

Systems Programming in Parallel Logic Languages

Software

A guide to cloud computing for students, scientists, and engineers, with advice and many hands-on examples.
The emergence of powerful, always-on cloud utilities has transformed how consumers interact with information technology, enabling video streaming, intelligent personal assistants, and the sharing of content. Businesses, too, have benefited from the cloud, outsourcing much of their information technology to cloud services. Science, however, has not fully exploited the advantages of the cloud. Could scientific discovery be accelerated if mundane chores were automated and outsourced to the cloud? Leading computer scientists Ian Foster and Dennis Gannon argue that it can, and in this book offer a guide to cloud computing for students, scientists, and engineers, with advice and many hands-on examples. 
The book surveys the technology that underpins the cloud, new approaches to technical problems enabled by the cloud, and the concepts required to integrate cloud services into scientific work. It covers managing data in the cloud, and how to program these services; computing in the cloud, from deploying single virtual machines or containers to supporting basic interactive science experiments to gathering clusters of machines to do data analytics; using the cloud as a platform for automating analysis procedures, machine learning, and analyzing streaming data; building your own cloud with open source software; and cloud security.
The book is accompanied by a website, Cloud4SciEng.org, that provides a variety of supplementary material, including exercises, lecture slides, and other resources helpful to readers and instructors.

Cloud Computing for Science and Engineering (Scientific and Engineering Computation)

Gannon, Dennis B.

Computer Science

The MIT Press

Big Data and Social Science: Data Science Methods and Tools for Research and Practice (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

Big Data and Social Science: Data Science Methods and Tools for Research and Practice, Second Editionshows how to apply data science to real-world problems, covering all stages of a data-intensive social science or policy project. Prominent leaders in the social sciences, statistics, and computer science as well as the field of data science provide a unique perspective on how to apply modern social science research principles and current analytical and computational tools. The text teaches you how to identify and collect appropriate data, apply data science methods and tools to the data, and recognize and respond to data errors, biases, and limitations. 
Features: Takes an accessible, hands-on approach to handling new types of data in the social sciences Presents the key data science tools in a non-intimidating way to both social and data scientists while keeping the focus on research questions and purposes Illustrates social science and data science principles through real-world problems Links computer science concepts to practical social science research Promotes good scientific practice Provides freely available workbooks with data, code, and practical programming exercises, through Binder and GitHub 
New to the Second Edition: Increased use of examples from different areas of social sciences New chapter on dealing with Bias and Fairness in Machine Learning models Expanded chapters focusing on Machine Learning and Text Analysis Revamped hands-on Jupyter notebooks to reinforce concepts covered in each chapter 
This classroom-tested book fills a major gap in graduate- and professional-level data science and social science education. It can be used to train a new generation of social data scientists to tackle real-world problems and improve the skills and competencies of applied social scientists and public policy practitioners. It empowers you to use the massive and rapidly growing amounts of available data to interpret economic and social activities in a scientific and rigorous manner.

Kreuter, Frauke

Lane, Julia

Ghani, Rayid

Jarmin, Ron S.

Reference

Chapman and Hall/CRC

Product Description 
Big Data and Social Science: Data Science Methods and Tools for Research and Practice, Second Edition shows how to apply data science to real-world problems, covering all stages of a data-intensive social science or policy project. Prominent leaders in the social sciences, statistics, and computer science as well as the field of data science provide a unique perspective on how to apply modern social science research principles and current analytical and computational tools. The text teaches you how to identify and collect appropriate data, apply data science methods and tools to the data, and recognize and respond to data errors, biases, and limitations. 
Features:\nTakes an accessible, hands-on approach to handling new types of data in the social sciences 
Presents the key data science tools in a non-intimidating way to both social and data scientists while keeping the focus on research questions and purposes 
Illustrates social science and data science principles through real-world problems 
Links computer science concepts to practical social science research 
Promotes good scientific practice 
Provides freely available with data, code, and practical programming exercises, through Binder and GitHub\nNew to the Second Edition:\nIncreased use of examples from different areas of social sciences 
New chapter on dealing with Bias and Fairness in Machine Learning models 
Expanded chapters focusing on Machine Learning and Text Analysis 
Revamped hands-on Jupyter notebooks to reinforce concepts covered in each chapter\nThis classroom-tested book fills a major gap in graduate- and professional-level data science and social science education. It can be used to train a new generation of social data scientists to tackle real-world problems and improve the skills and competencies of applied social scientists and public policy practitioners. It empowers you to use the massive and rapidly growing amounts of available data to interpret economic and social activities in a scientific and rigorous manner. 
 Review 
"Like the first edition, the new edition will continue to play an important role for the intended audience and a wider professional community. The much-needed second edition is timely and showcases a wide range of examples and application examples from different areas of the social sciences to demonstrate how the methods are implemented using several real datasets. As expected with this kind of book, the topics of this text are diverse in nature, but interesting none the less. As it is well known, machine learning techniques are subject to inherited bias in model selection and consequently negatively impacts post estimation and prediction. This new edition includes a new chapter on dealing with bias and fairness in machine learning models, a much-needed fair and welcome edition! Further, the authors have done an excellent job in expanding the material on machine learning and text analysis. Like the first edition, the main strength of the book is that it offers a wide variety of applications that are based on real datasets emerging from social science perspectives and useful for both academic and professional communes. As Jupyter has become more popular as the data scientists’ computational notebook of choice, the book has new and improved hands-on Jupyter notebooks to complement each chapter’s material. In conclusion, this new edition has an impressive collection of material on useful and interesting topics on big data. The book will be equally useful to graduate students and researchers interested in gaining perspectives and knowledge on this important topic. The new volume comprises of a wealth of information, a kind of one-stop shop, and can be served as a textbook and research reference book."- S. Ejaz Ahmed, Brock University, Canada\nPraise For First Edition 
"This book builds a nice bridge connecting social science and big data methodology. Big data such as social media and electronic health records,

Use the Power of Leverage to Scale Your Business, and Remove the Ceiling on How Much Money You Can Generate and How Many People You Can Impact!\nWhen you began your journey as an entrepreneur, you probably planned to build your business through hard work, grit, and determination.\nAll of those are great, and they can help you reach your goals.\nBut they’re not everything. If you rely on those things alone, you’ll likely create success … but chances are, you’ll also be exhausted, stressed, and limited in terms of income and reach.\nOn the other hand, when you create true leverage—in every area of your business—you can scale your company as big as you want to!\nIt is possible when you have the right strategies and mindset in place.\nInside the pages of Leverage Your Expertise: 16 Entrepreneurs Share Their Small Business Success Stories, you’ll discover exactly what you need to do so you can enjoy plenty of free time AND a great income while you share your expertise with those who need it.\nPlus, businesspeople from a variety of industries share the stories of leveraging their expertise through the power of creating group online programs and courses.\nThese smart, savvy entrepreneurs have discovered powerful ways to leverage their time, connections, resources, circles of influence, unique backgrounds, and opportunities … to supercharge their income and take full control of their time!\nMeanwhile, they’re changing the lives of so many people.\nAnd they’re living their wildest entrepreneurial dreams.\nYou can, too.\nIf you’re ready for inspiration and motivation, as well as powerful ideas to put leverage to work for you, so you can enjoy time freedom, increase your profits, and help more people, order your copy today.

Leverage Your Expertise: 16 Entrepreneurs Share Their Small Business Success Stories and Lessons Learned (Expertise-Based Business)

Business & Money

Vincent, Alina

Baak, Huda

Cheng, Dr. Jane

Barnes, Candas

Arnold, Erin

Chencinski, Aneta

Alexander, Diann

Gélinas, Carmen

Gibson, Marie

Business Development & Entrepreneurship

Business Success Edge

Own Your Expertise: 13 Entrepreneurs Share Inspiring Stories About Success and Lessons Learned in Their Business (Expertise-Based Business)

Lawson, Barbara

Newman, Stephanie

Callender, Florence

Conyers, Tricia

Wendel, Nedra

Yagos, Jennifer

Boyce, Blanche

Seidelman, Jennifer

Industries

The familiar image of the Habsburg Army as a last supranational institution holding the Austro-Hungarian Monarchy together was decisively shaped by the pro-military literature and journalism of the pre-1914 period. Many of the Austrian writers who forged that image in countless feuilletons, stories and novels have long since been forgotten. This study aims to reconstruct the ideological context of military fictions and fiction on military themes and by doing so contribute to the debate concerning texts that are already well-known.

The Image of the Habsburg Army in Austrian Prose Fiction, 1888 to 1914 (Europäische Hochschulschriften / European University Studies / Publications Universitaires Européennes)

Literature & Fiction

History & Criticism

Peter Lang International Academic Publishers

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Books by Foster, Ian