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2018-2019 IEEE Projects on Data Science

Data Science-2018

The era of analytics,data science, and big data has driven substantial governmental, industrial’ and disciplinary interest; goal and strategy transformation; and a paradigm shift in research and innovation. This has resulted in significant opportunities and prospects becoming available, and an overwhelming amount of fanfare has spread across domains, areas, and events. A review of the related initiatives, progress, and status of data science, analytics, and big data4 and the diversified discussions about the prospects, challenges, and directions5 makes clear the controversy caused by the potential conflict of these various elements. There is a need for deep discussions about the nature and pitfalls of data science, clarification of fundamental concepts and myths, and a demonstration of the intrinsic characteristics and opportunities of data science. Thus, this article focuses on two fundamental issues—the nature and pitfalls of data science. I highlight the status, intrinsic factors, characteristics, and features of the era of data science and analytics, as well as the challenges and opportunities in innovation, research, and disciplinary development. I also summarize common pitfalls about the concepts of data science, data volume, infrastructure, analytics, and capabilities and roles. Building on these discussions, I then present the concepts and possible future directions of data science.

2018-2019 IEEE Projects on Data Science

50% OFFER Features of the Data Science Era Identifying the features and characteristics of the data science era is critical and challenging. Let's explore this from the perspective of the transformation and paradigm shift caused by data science and discuss the core driving forces and the status of several typical issues confronting the data science field.

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Data Science-2018 (ML) is now widespread. Traditional software engineering can be applied to the development ML applications. However, we have to consider specific problems with ML applications in therms of their quality. In this paper, we present a survey of software quality for ML applications to consider the quality of ML applications as an emerging discussion. From this survey, we raised problems with ML applications and discovered software engineering approaches and software testing research areas to solve these problems. We classified survey targets into Academic Conferences, Magazines, and Communities. We targeted 16 academic conferences on artificial intelligence and software engineering, including 78 papers. We targeted 5 Magazines, including 22 papers. The results indicated key areas, such as deep learning, fault localization, and prediction, to be researched with software engineering and testing.