... the increasing entanglement of computational processes with organizational life. Algorithms artificial intelligence sociomateriality algorithmic management data science human–machine interaction Algorithms are ubiquitous in everyday life and organizing. Few work processes and operations function without...

...Abstract Exploring the game-changing potential of the data science and artificial intelligence (AI) in the healthcare sector, this chapter specifically looks into the use cases to develop practical guidelines for the industry to implement the technology at a higher level. By analysing the use...

...Abstract Data science and artificial intelligence (AI) have become popular in recent decades and boosted innovation in various sectors due to its real-life applications. An enormous amount of data produced in various sectors can be utilized to produce meaningful insights about the data using data...

...Abstract Harnessing the combined potential of artificial intelligence (AI) and data science is opening new opportunities and growth avenues for stakeholders across the industrial domains. By using real-world cases, this chapter emphasises combining AI with data science can offer decisive benefits...

... of analytical tools to analyze and manipulate large data sets to succeed in a data science world. A statistical technique available to accountants to perform predictive and prescriptive analytics is Monte Carlo simulation. This chapter illustrates how to use Monte Carlo simulation in developing a probabilistic...

... Furniture Manufacturing, Data Science and Inclusive Management. Outlining of the Methodology Phase I. Analysis of Customised Production Processes The objectives of this phase are to select the main theoretical principles of the standardisation processes of furniture production and early pricing...

... evidence to support the decision. Olympics cost overrun policy data science prediction stadium 1. Introduction The Brisbane 2032 Olympic bid says that Brisbane’s Olympic officials are confident of breaking even, and avoiding building stadiums that end up as white elephants. The proposed budget...

... of any work in data science, the focus of this book will be to identify disparities in access to palliative care across racial and social groups in the US. We will propose a system which will use characteristics found in readily sourced large data sets to identify individuals who may not ordinarily...

... wrangling, data visualisation, data mining, machine learning, deep learning, etc. are the outcomes of earlier deliberations on big data and the digital revolution. Subsequently, these sub-topics, tools and techniques got submerged under a new discipline called ‘data science’. The acceptance of data science...

..., the authors highlight numerous theoretical and empirical opportunities and considerations that this perspective holds for future HRM scholarship. Big data complexity dynamism data science meta-attributes of big data Introduction Big data possess the potential to “transform our lives as much...

... such as how to optimise marketing strategies and how to enhance customer experience, which has resulted in the most significant goal of improving operational efficiency in the financial industry. Big data life insurance data science data analytics emerging technologies insurance 1. Introduction Today...

...Abstract This chapter conceptualizes computational methods across three related, yet distinct approaches: (1) Social Simulation, (2) Data Science, and (3) Big Data. Group communication research is then situated and reviewed along these three lines of research. Although some areas have considerable...

... implementation. Machine learning artificial intelligence big data neural networks deep learning data science The Big Data Era Although it probably went unnoticed at the time, it is likely that the first Data Scientists were working thousands of years ago, not too long after the development of written...

...Data science platforms like Kaggle provide many opportunities; they offer benefits to hosts (e.g., companies, academic institutes), as well as to data science enthusiasts. The main opportunities as well as challenges are summarized in Table 14.1 and described in more detail in the following...