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The rapid increase in data volumes in companies has meant that momentous and comprehensive information gathering is barely possible by manual means. Business intelligence solutions can help here. They provide tools with appropriate…
The availability of large amounts of data together with advances in analytical techniques afford an opportunity to address difficult challenges in ensuring that healthcare is safe, effective, efficient, patient-centered, equitable, and…
This paper reviews literature pertaining to the development of data science as a discipline, current issues with data bias and ethics, and the role that the discipline of information science may play in addressing these concerns.…
Science has a data management problem, as well as a project management problem. While industrial-grade data science teams have embraced the agile mindset, and adopted or created all kind of tools to create reproducible workflows,…
Big data refers to large and complex data sets that, under existing approaches, exceed the capacity and capability of current compute platforms, systems software, analytical tools and human understanding. Numerous lessons on the scalability…
Programming is ubiquitous in applied biostatistics; adopting software engineering skills will help biostatisticians do a better job. To explain this, we start by highlighting key challenges for software development and application in…
Scientific and technological progress has historically been very beneficial to humanity but this does not always need to be true. Going forward, science may enable bad actors to cause genetically engineered pandemics that are more frequent…
Data collection is pervasively bound to our digital lifestyle. A recent study by the IDC reports that the growth of the data created and replicated in 2020 was even higher than in the previous years due to pandemic-related confinements to…
Big data systems address the challenges of capturing, storing, managing, analyzing, and visualizing big data. Within this context, developing benchmarks to evaluate and compare big data systems has become an active topic for both research…
Bayesian data analysis (BDA) is today used by a multitude of research disciplines. These disciplines use BDA as a way to embrace uncertainty by using multilevel models and making use of all available information at hand. In this chapter, we…
Data sharing partnerships are increasingly an imperative for research institutions and, at the same time, a challenge for established models of data governance and ethical research oversight. We analyse four cases of data partnership…
Big Data is reforming many industrial domains by providing decision support through analyzing large data volumes. Big Data testing aims to ensure that Big Data systems run smoothly and error-free while maintaining the performance and…
The unprecedented growth in the availability of data of all types and qualities and the emergence of the field of data science has provided an impetus to finally realizing the implementation of the full breadth of the Nolan and Temple Lang…
Data science has arrived, and computational statistics is its engine. As the scale and complexity of scientific and industrial data grow, the discipline of computational statistics assumes an increasingly central role among the statistical…
Advances in data analytics bring with them civil rights implications. Data-driven and algorithmic decision making increasingly determine how businesses target advertisements to consumers, how police departments monitor individuals or…
Datasets have played a foundational role in the advancement of machine learning research. They form the basis for the models we design and deploy, as well as our primary medium for benchmarking and evaluation. Furthermore, the ways in which…
In this paper we have focused a variety of techniques, approaches and different areas of the research which are helpful and marked as the important field of data mining Technologies. As we are aware that many Multinational companies and…
The proliferation of deep learning techniques led to a wide range of advanced analytics applications in important business areas such as predictive maintenance or product recommendation. However, as the effectiveness of advanced analytics…
This project addresses the challenges of responsible and fair resource allocation in data science (DS), focusing on DS queries evaluation. Current DS practices often overlook the broader socio-economic, environmental, and ethical…
Herein we review aspects of leading-edge research and innovation in chemistry which exploits big data and machine learning (ML), two computer science fields that combine to yield machine intelligence. ML can accelerate the solution of…