This paper reports on our review of published research relating to how teams work together
to execute Big Data projects. Our findings suggest that there is no agreed upon standard for …

Data science has employed great research efforts in developing advanced analytics,
improving data models and cultivating new algorithms. However, not many authors have …

Data Science projects aim to methodologically extract knowledge and value from data to
help organizations to improve performance. Dedicated process models are applied to …

Data Science is an emerging field with a significant research focus on improving the
techniques available to analyze data. However, there has been much less focus on how …

There are many reasons data science teams should use a well-defined process to manage
and coordinate their efforts, such as improved collaboration, efficiency and stakeholder …

The challenge in executing a data science project is more than just identifying the best
algorithm and tool set to use. Additional sociotechnical challenges include items such as …

Artificial Intelligence (AI) in Smart Agricultural Facilities (SAF) often lacks explainability,
hindering farmers from taking full advantage of their capabilities. This study tackles this gap …

Agile software development follows an adaptive and iterative approach. However, the
management of data (eg, development data or product data) can pose significant challenges …

The rise of big data has led to an increase in data science projects conducted by
organizations. Such projects aim to create valuable insights by improving decision making …

The irreplaceable key to the triumph of Question & Answer (Q & A) platforms is their users
providing high-quality answers to the challenging questions posted across various topics of …