Related papers: Data Science Methodologies: Current Challenges and…

A growing number of students are completing undergraduate degrees in statistics and entering the workforce as data analysts. In these positions, they are expected to understand how to utilize databases and other data warehouses, scrape data…

The recent efforts in automation of machine learning or data science has achieved success in various tasks such as hyper-parameter optimization or model selection. However, key areas such as utilizing domain knowledge and data semantics are…

Data science is an integrated workflow of technical, analytical, communication, and ethical skills, but current AI benchmarks focus mostly on constituent parts. We test whether AI models can generate end-to-end data science projects. To do…

This book chapter attempts to counter anxieties in the humanities and social science about the role of big data in research by focusing on approaches which, by being firmly grounded in the traditional values of disciplines, enhance existing…

Rapidly evolving technology, data and analytic landscapes are permeating many fields and professions. In public health, the need for data science skills including data literacy is particularly prominent given both the potential of novel…

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.…

Progress in science is deeply bound to the effective use of high-performance computing infrastructures and to the efficient extraction of knowledge from vast amounts of data. Such data comes from different sources that follow a cycle…

With the increasing amount of data globally, analyzing and visualizing data are becoming essential skills across various professions. It is important to equip university students with these essential data skills. To learn, design, and…

Context: Data mining techniques have demonstrated to be a powerful technique for discovering insights hidden in data from a domain. However, these techniques demand very specialised skills. People willing to analyse data often lack these…

With the shifting focus of organizations and governments towards digitization of academic and technical documents, there has been an increasing need to use this reserve of scholarly documents for developing applications that can facilitate…

The adoption of data science brings vast benefits to Small and Medium-sized Enterprises (SMEs) including business productivity, economic growth, innovation and jobs creation. Data Science can support SMEs to optimise production processes,…

The growth of the data science field requires better tools to understand such a fast-paced growing domain. Moreover, individuals from different backgrounds became interested in following a career as data scientists. Therefore, providing a…

Although data science builds on knowledge from computer science, mathematics, statistics, and other disciplines, data science is a unique field with many mysteries to unlock: challenging scientific questions and pressing questions of…

Data is a precious resource in today's society, and is generated at an unprecedented and constantly growing pace. The need to store, analyze, and make data promptly available to a multitude of users introduces formidable challenges in…

Recent advances in large language models (LLMs) have enabled a new class of AI agents that automate multiple stages of the data science workflow by integrating planning, tool use, and multimodal reasoning across text, code, tables, and…

A fundamental problem in the practice and teaching of data science is how to evaluate the quality of a given data analysis, which is different than the evaluation of the science or question underlying the data analysis. Previously, we…

Context: User-Centered Design and Agile methodologies focus on human issues. Nevertheless, agile methodologies focus on contact with contracting customers and generating value for them. Usually, the communication between end users and the…

The rising availability of digital traces provides a fertile ground for new solutions to both, new and old problems in cities. Even though a massive data set analyzed with Data Science methods may provide a powerful solution to a problem,…

Several authors, including the American Statistician (ASA), have noted the challenges facing statisticians when attacking large, complex, unstructured problems, as opposed to well-defined textbook problems. Clearly, the standard paradigm of…

As we are fast approaching the beginning of a paradigm shift in the field of science, Data driven science (the so called fourth science paradigm) is going to be the driving force in research and innovation. From medicine to biodiversity and…