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Perspectives on Data Science for Software Engineering

Perspectives on Data Science for Software Engineering
  • Author : Tim Menzies
  • Publsiher : Morgan Kaufmann
  • Release : 14 July 2016
  • ISBN : 0128042613
  • Pages : 408 pages
  • Rating : 5/5 from 1 ratings
GET THIS BOOKPerspectives on Data Science for Software Engineering

Summary:
Perspectives on Data Science for Software Engineering presents the best practices of seasoned data miners in software engineering. The idea for this book was created during the 2014 conference at Dagstuhl, an invitation-only gathering of leading computer scientists who meet to identify and discuss cutting-edge informatics topics. At the 2014 conference, the concept of how to transfer the knowledge of experts from seasoned software engineers and data scientists to newcomers in the field highlighted many discussions. While there are many books covering data mining and software engineering basics, they present only the fundamentals and lack the perspective that comes from real-world experience. This book offers unique insights into the wisdom of the community’s leaders gathered to share hard-won lessons from the trenches. Ideas are presented in digestible chapters designed to be applicable across many domains. Topics included cover data collection, data sharing, data mining, and how to utilize these techniques in successful software projects. Newcomers to software engineering data science will learn the tips and tricks of the trade, while more experienced data scientists will benefit from war stories that show what traps to avoid. Presents the wisdom of community experts, derived from a summit on software analytics Provides contributed chapters that share discrete ideas and technique from the trenches Covers top areas of concern, including mining security and social data, data visualization, and cloud-based data Presented in clear chapters designed to be applicable across many domains


Perspectives on Data Science for Software Engineering

Perspectives on Data Science for Software Engineering
  • Author : Tim Menzies,Laurie Williams,Thomas Zimmermann
  • Publisher : Morgan Kaufmann
  • Release : 14 July 2016
GET THIS BOOKPerspectives on Data Science for Software Engineering

Perspectives on Data Science for Software Engineering presents the best practices of seasoned data miners in software engineering. The idea for this book was created during the 2014 conference at Dagstuhl, an invitation-only gathering of leading computer scientists who meet to identify and discuss cutting-edge informatics topics. At the 2014 conference, the concept of how to transfer the knowledge of experts from seasoned software engineers and data scientists to newcomers in the field highlighted many discussions. While there are many books covering


The Art and Science of Analyzing Software Data

The Art and Science of Analyzing Software Data
  • Author : Christian Bird,Tim Menzies,Thomas Zimmermann
  • Publisher : Elsevier
  • Release : 02 September 2015
GET THIS BOOKThe Art and Science of Analyzing Software Data

The Art and Science of Analyzing Software Data provides valuable information on analysis techniques often used to derive insight from software data. This book shares best practices in the field generated by leading data scientists, collected from their experience training software engineering students and practitioners to master data science. The book covers topics such as the analysis of security data, code reviews, app stores, log files, and user telemetry, among others. It covers a wide variety of techniques such as


Build a Career in Data Science

Build a Career in Data Science
  • Author : Emily Robinson,Jacqueline Nolis
  • Publisher : Manning Publications
  • Release : 24 March 2020
GET THIS BOOKBuild a Career in Data Science

Summary You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology What are the keys to a data scientist’s


Challenges and Applications of Data Analytics in Social Perspectives

Challenges and Applications of Data Analytics in Social Perspectives
  • Author : Sathiyamoorthi, V.,Elci, Atilla
  • Publisher : IGI Global
  • Release : 04 December 2020
GET THIS BOOKChallenges and Applications of Data Analytics in Social Perspectives

With exponentially increasing amounts of data accumulating in real-time, there is no reason why one should not turn data into a competitive advantage. While machine learning, driven by advancements in artificial intelligence, has made great strides, it has not been able to surpass a number of challenges that still prevail in the way of better success. Such limitations as the lack of better methods, deeper understanding of problems, and advanced tools are hindering progress. Challenges and Applications of Data Analytics


Computing and Software Science

Computing and Software Science
  • Author : Bernhard Steffen,Gerhard Woeginger
  • Publisher : Springer Nature
  • Release : 04 October 2019
GET THIS BOOKComputing and Software Science

The papers of this volume focus on the foundational aspects of computer science, the thematic origin and stronghold of LNCS, under the title “Computing and Software Science: State of the Art and Perspectives”. They are organized in two parts: The first part, Computation and Complexity, presents a collection of expository papers on fashionable themes in algorithmics, optimization, and complexity. The second part, Methods, Languages and Tools for Future System Development, aims at sketching the methodological evolution that helps guaranteeing that


Agile Data Science 2.0

Agile Data Science 2.0
  • Author : Russell Jurney
  • Publisher : "O'Reilly Media, Inc."
  • Release : 07 June 2017
GET THIS BOOKAgile Data Science 2.0

Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they’re to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools. Author Russell Jurney demonstrates how to compose a data platform for building, deploying, and refining analytics applications with


Software Engineering Foundations

Software Engineering Foundations
  • Author : Yingxu Wang
  • Publisher : CRC Press
  • Release : 09 August 2007
GET THIS BOOKSoftware Engineering Foundations

A groundbreaking book in this field, Software Engineering Foundations: A Software Science Perspective integrates the latest research, methodologies, and their applications into a unified theoretical framework. Based on the author's 30 years of experience, it examines a wide range of underlying theories from philosophy, cognitive informatics, denota


Perspectives on the Future of Software Engineering

Perspectives on the Future of Software Engineering
  • Author : Jürgen Münch,Klaus Schmid
  • Publisher : Springer Science & Business Media
  • Release : 13 June 2013
GET THIS BOOKPerspectives on the Future of Software Engineering

The dependence on quality software in all areas of life is what makes software engineering a key discipline for today’s society. Thus, over the last few decades it has been increasingly recognized that it is particularly important to demonstrate the value of software engineering methods in real-world environments, a task which is the focus of empirical software engineering. One of the leading protagonists of this discipline worldwide is Prof. Dr. Dr. h.c. Dieter Rombach, who dedicated his entire


Responsible Analytics and Data Mining in Education

Responsible Analytics and Data Mining in Education
  • Author : Badrul H. Khan,Joseph Rene Corbeil,Maria Elena Corbeil
  • Publisher : Routledge
  • Release : 07 December 2018
GET THIS BOOKResponsible Analytics and Data Mining in Education

Winner of two Outstanding Book Awards from the Association of Educational Communications and Technology (Culture, Learning, & Technology and Systems Thinking & Change divisions)! Rapid advancements in our ability to collect, process, and analyze massive amounts of data along with the widespread use of online and blended learning platforms have enabled educators at all levels to gain new insights into how people learn. Responsible Analytics and Data Mining in Education addresses the thoughtful and purposeful navigation, evaluation, and implementation of these emerging


Doing Data Science

Doing Data Science
  • Author : Cathy O'Neil,Rachel Schutt
  • Publisher : "O'Reilly Media, Inc."
  • Release : 09 October 2013
GET THIS BOOKDoing Data Science

Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay


Building Data Science Teams

Building Data Science Teams
  • Author : DJ Patil
  • Publisher : "O'Reilly Media, Inc."
  • Release : 15 September 2011
GET THIS BOOKBuilding Data Science Teams

As data science evolves to become a business necessity, the importance of assembling a strong and innovative data teams grows. In this in-depth report, data scientist DJ Patil explains the skills, perspectives, tools and processes that position data science teams for success. Topics include: What it means to be "data driven." The unique roles of data scientists. The four essential qualities of data scientists. Patil's first-hand experience building the LinkedIn data science team.


An Introduction to Self-adaptive Systems

An Introduction to Self-adaptive Systems
  • Author : Danny Weyns
  • Publisher : John Wiley & Sons
  • Release : 01 February 2021
GET THIS BOOKAn Introduction to Self-adaptive Systems

A concise and practical introduction to the foundations and engineering principles of self-adaptation Though it has recently gained significant momentum, the topic of self-adaptation remains largely under-addressed in academic and technical literature. This book changes that. Using a systematic and holistic approach, An Introduction to Self-adaptive Systems: A Contemporary Software Engineering Perspective provides readers with an accessible set of basic principles, engineering foundations, and applications of self-adaptation in software-intensive systems. It places self-adaptation in the context of techniques like uncertainty


Smart Data

Smart Data
  • Author : Kuan-Ching Li,Beniamino Di Martino,Laurence T. Yang,Qingchen Zhang
  • Publisher : CRC Press
  • Release : 19 March 2019
GET THIS BOOKSmart Data

Smart Data: State-of-the-Art Perspectives in Computing and Applications explores smart data computing techniques to provide intelligent decision making and prediction services support for business, science, and engineering. It also examines the latest research trends in fields related to smart data computing and applications, including new computing theories, data mining and machine learning techniques. The book features contributions from leading experts and covers cutting-edge topics such as smart data and cloud computing, AI for networking, smart data deep learning, Big Data


Contemporary Empirical Methods in Software Engineering

Contemporary Empirical Methods in Software Engineering
  • Author : Michael Felderer,Guilherme Horta Travassos
  • Publisher : Anonim
  • Release : 27 February 2021
GET THIS BOOKContemporary Empirical Methods in Software Engineering

This book presents contemporary empirical methods in software engineering related to the plurality of research methodologies, human factors, data collection and processing, aggregation and synthesis of evidence, and impact of software engineering research. The individual chapters discuss methods that impact the current evolution of empirical software engineering and form the backbone of future research. Following an introductory chapter that outlines the background of and developments in empirical software engineering over the last 50 years and provides an overview of the subsequent


Agile Data Science

Agile Data Science
  • Author : Russell Jurney
  • Publisher : "O'Reilly Media, Inc."
  • Release : 15 October 2013
GET THIS BOOKAgile Data Science

Mining big data requires a deep investment in people and time. How can you be sure you’re building the right models? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Hadoop. Using lightweight tools such as Python, Apache Pig, and the D3.js library, your team will create an agile environment for exploring data, starting with an example application to mine your own email inboxes. You’ll learn an iterative