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Article
Publication date: 10 August 2015

Panagiotis Barlas, Ivor Lanning and Cathal Heavey

Data science is the study of the generalizable extraction of knowledge from data. It includes a variety of components and develops on methods and concepts from many…

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2150

Abstract

Purpose

Data science is the study of the generalizable extraction of knowledge from data. It includes a variety of components and develops on methods and concepts from many domains, containing mathematics, probability models, machine learning, statistical learning, computer programming, data engineering, pattern recognition and learning, visualization and data warehousing aiming to extract value from data. The purpose of this paper is to provide an overview of open source (OS) data science tools, proposing a classification scheme that can be used to study OS data science software.

Design/methodology/approach

The proposed classification scheme is based on general characteristics, project activity, operational characteristics and data mining characteristics. The authors then use the proposed scheme to examine 70 identified Open Source Software. From this the authors provide insight about the current status of OS data science tools and reveal the state-of-the-art tools.

Findings

The features of 70 OS tools are recorded based on the criteria of the four group characteristics, general characteristics, project activity, operational characteristics and data mining characteristics. Interesting results came from the analysis of these features and are recorded here.

Originality/value

The contribution of this survey is development of a new classification scheme for examination and study of OS data science tools. In parallel, this study provides an overview of existing OS data science tools.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 8 no. 3
Type: Research Article
ISSN: 1756-378X

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Article
Publication date: 13 October 2020

Sirje Virkus and Emmanouel Garoufallou

The purpose of this paper is to present the results of a study exploring the emerging field of data science from the library and information science (LIS) perspective.

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1203

Abstract

Purpose

The purpose of this paper is to present the results of a study exploring the emerging field of data science from the library and information science (LIS) perspective.

Design/methodology/approach

Content analysis of research publications on data science was made of papers published in the Web of Science database to identify the main themes discussed in the publications from the LIS perspective.

Findings

A content analysis of 80 publications is presented. The articles belonged to the six broad categories: data science education and training; knowledge and skills of the data professional; the role of libraries and librarians in the data science movement; tools, techniques and applications of data science; data science from the knowledge management perspective; and data science from the perspective of health sciences. The category of tools, techniques and applications of data science was most addressed by the authors, followed by data science from the perspective of health sciences, data science education and training and knowledge and skills of the data professional. However, several publications fell into several categories because these topics were closely related.

Research limitations/implications

Only publication recorded in the Web of Science database and with the term “data science” in the topic area were analyzed. Therefore, several relevant studies are not discussed in this paper that either were related to other keywords such as “e-science”, “e-research”, “data service”, “data curation”, “research data management” or “scientific data management” or were not present in the Web of Science database.

Originality/value

The paper provides the first exploration by content analysis of the field of data science from the perspective of the LIS.

Details

Data Technologies and Applications, vol. 54 no. 5
Type: Research Article
ISSN: 2514-9288

Keywords

Abstract

Details

Culturally Responsive Strategies for Reforming STEM Higher Education
Type: Book
ISBN: 978-1-78743-405-9

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Article
Publication date: 19 February 2021

Muhammad Javed Ramzan, Saif Ur Rehman Khan, Inayat ur-Rehman, Muhammad Habib Ur Rehman and Ehab Nabiel Al-khannaq

In recent years, data science has become a high-demand profession, thereby attracting transmuters (individuals who want to change their profession due to industry trends…

Abstract

Purpose

In recent years, data science has become a high-demand profession, thereby attracting transmuters (individuals who want to change their profession due to industry trends) to this field. The primary purpose of this paper is to guide transmuters in becoming data scientists.

Design/methodology/approach

An exploratory study was conducted to uncover the challenges faced by data scientists according to their educational backgrounds. An extensive set of responses from 31 countries was received.

Findings

The results reveal that skill requirements and tool usage vary significantly with educational background. However, regardless of differences in academic background, the data scientists surveyed spend more time analyzing data than operationalizing insight.

Research limitations/implications

The collected data are available to support replication in various scenarios, for example, for use as a roadmap for those with an educational background in art-related disciplines. Additional empirical studies can also be conducted specific to geographical location.

Practical implications

The current work has categorized data scientists by their fields of study making it easier for universities and online academies to suggest required knowledge (courses) according to prospective students' educational background.

Originality/value

The conducted study suggests the required knowledge and skills for transmuters to acquire, based on their educational background, and reports a set of motivational factors attracting them to adopt the data science field.

Details

Library Hi Tech, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0737-8831

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Article
Publication date: 10 November 2020

Atri Sengupta, Shashank Mittal and Kuchi Sanchita

Rapid advancement of data science has disrupted both business and employees in organizations. However, extant literature primarily focuses on the organizational level…

Abstract

Purpose

Rapid advancement of data science has disrupted both business and employees in organizations. However, extant literature primarily focuses on the organizational level phenomena, and has almost ignored the employee/individual perspective. This study thereby intends to capture the experiences of mid-level managers about these disruptions vis a vis their corresponding actions.

Design/methodology/approach

In a small-sample qualitative research design, Interpretative Phenomenological Analysis (IPA) was adopted to capture this individual-level phenomenon. Twelve mid-level managers from large-scale Indian organizations that have extensively adopted data science tools and techniques participated in a semi-structured and in-depth interview process.

Findings

Our findings unfolded several perspectives gained from their experiences, leading thereby to two emergent person-job (mis)fit process models. (1) Managers, who perceived demands-abilities misfit (D-A misfit) as a growth-alignment opportunity vis a vis their corresponding actions, which effectively trapped them into a vicious cycle; and (2) the managers, who considered D-A misfit as a psychological strain vis a vis their corresponding actions, which engaged them into a benevolent cycle.

Research limitations/implications

The present paper has major theoretical and managerial implications in the field of human resource management and business analytics.

Practical implications

The findings advise managers that the focus should be on developing an organizational learning eco-system, which would enable mid-level managers to gain their confidence and control over their job and work environment in the context of data science disruptions. Importantly, organizations should facilitate integrated workplace learning (both formal and informal) with an appropriate ecosystem to help mid-level managers to adapt to the data-science disruptions.

Originality/value

The present study offers two emergent cyclic models to the existing person–job fit literature in the context of data science disruptions. A scant attention of the earlier researchers on how individual employees actually experience disruption, and the corresponding IPA method used in the present study may add significant value to the extant literature. Further, it opens a timely and relevant future research avenues in the context of data science disruptions.

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Article
Publication date: 4 September 2019

Sirje Virkus and Emmanouel Garoufallou

Data science is a relatively new field which has gained considerable attention in recent years. This new field requires a wide range of knowledge and skills from different…

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1323

Abstract

Purpose

Data science is a relatively new field which has gained considerable attention in recent years. This new field requires a wide range of knowledge and skills from different disciplines including mathematics and statistics, computer science and information science. The purpose of this paper is to present the results of the study that explored the field of data science from the library and information science (LIS) perspective.

Design/methodology/approach

Analysis of research publications on data science was made on the basis of papers published in the Web of Science database. The following research questions were proposed: What are the main tendencies in publication years, document types, countries of origin, source titles, authors of publications, affiliations of the article authors and the most cited articles related to data science in the field of LIS? What are the main themes discussed in the publications from the LIS perspective?

Findings

The highest contribution to data science comes from the computer science research community. The contribution of information science and library science community is quite small. However, there has been continuous increase in articles from the year 2015. The main document types are journal articles, followed by conference proceedings and editorial material. The top three journals that publish data science papers from the LIS perspective are the Journal of the American Medical Informatics Association, the International Journal of Information Management and the Journal of the Association for Information Science and Technology. The top five countries publishing are USA, China, England, Australia and India. The most cited article has got 112 citations. The analysis revealed that the data science field is quite interdisciplinary by nature. In addition to the field of LIS the papers belonged to several other research areas. The reviewed articles belonged to the six broad categories: data science education and training; knowledge and skills of the data professional; the role of libraries and librarians in the data science movement; tools, techniques and applications of data science; data science from the knowledge management perspective; and data science from the perspective of health sciences.

Research limitations/implications

The limitations of this research are that this study only analyzed research papers in the Web of Science database and therefore only covers a certain amount of scientific papers published in the field of LIS. In addition, only publications with the term “data science” in the topic area of the Web of Science database were analyzed. Therefore, several relevant studies are not discussed in this paper that are not reflected in the Web of Science database or were related to other keywords such as “e-science,” “e-research,” “data service,” “data curation” or “research data management.”

Originality/value

The field of data science has not been explored using bibliographic analysis of publications from the perspective of the LIS. This paper helps to better understand the field of data science and the perspectives for information professionals.

Details

Data Technologies and Applications, vol. 53 no. 4
Type: Research Article
ISSN: 2514-9288

Keywords

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Article
Publication date: 22 June 2021

Pedro Jácome de Moura Jr

Data science lacks a distinctive identity and a theory-informed approach, both for its own sake and to properly be applied conjointly to the social sciences. This paper’s…

Abstract

Purpose

Data science lacks a distinctive identity and a theory-informed approach, both for its own sake and to properly be applied conjointly to the social sciences. This paper’s purposes are twofold: to provide (1) data science an illustration of theory adoption, able to address explanation and support prediction/prescription capacities and (2) a rationale for identification of the key phenomena and properties of data science so that the data speak through a contextual understanding of reality, broader than has been usual.

Design/methodology/approach

A literature review and a derived conceptual research model for a push–pull approach (adapted for a data science study in the management field) are presented. A real location–allocation problem is solved through a specific algorithm and explained in the light of the adapted push–pull theory, serving as an instance for a data science theory-informed application in the management field.

Findings

This study advances knowledge on the definition of data science key phenomena as not just pure “data”, but interrelated data and datasets properties, as well as on the specific adaptation of the push-pull theory through its definition, dimensionality and interaction model, also illustrating how to apply the theory in a data science theory-informed research. The proposed model contributes to the theoretical strengthening of data science, still an incipient area, and the solution of the location-allocation problem suggests the applicability of the proposed approach to broad data science problems, alleviating the criticism on the lack of explanation and the focus on pattern recognition in data science practice and research.

Research limitations/implications

The proposed algorithm requires the previous definition of a perimeter of interest. This aspect should be characterised as an antecedent to the model, which is a strong assumption. As for prescription, in this specific case, one has to take complementary actions, since theory, model and algorithm are not detached from in loco visits, market research or interviews with potential stakeholders.

Practical implications

This study offers a conceptual model for practical location–allocation problem analyses, based on the push–pull theoretical components. So, it suggests a proper definition for each component (the object, the perspective, the forces, its degrees and the nature of the movement). The proposed model has also an algorithm for computational implementation, which visually describes and explains components interaction, allowing further simulation (estimated forces degrees) for prediction.

Originality/value

First, this study identifies an overlap of push–pull theoretical approaches, which suggests theory adoption eventually as mere common sense, weakening further theoretical development. Second, this study elaborates a definition for the push–pull theory, a dimensionality and a relationship between its components. Third, a typical location–allocation problem is analysed in the light of the refactored theory, showing its adequacy for that class of problems. And fourth, this study suggests that the essence of a data science should be the study of contextual relationships among data, and that the context should be provided by the spatial, temporal, political, economic and social analytical interests.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

Keywords

Content available
Article
Publication date: 1 December 2020

Michela Arnaboldi, Andrea Robbiani and Paola Carlucci

Nearly 40 years since they first appeared, there is renewed interest in dashboards, engendered by the diffusion of business intelligence (BI) desktop software, such as…

Abstract

Purpose

Nearly 40 years since they first appeared, there is renewed interest in dashboards, engendered by the diffusion of business intelligence (BI) desktop software, such as Power BI, QlikView and Tableau, denoted collectively as “self-service” BI. Using these commodity software tools, the work to construct dashboards apparently becomes easier and more manageable and no longer requires the intervention of specialists. This paper aims to analyse the implementation of this kind of commodity dashboard in a university, exploring its role in performance management processes and investigating whether the dashboard affects the organisation (or not).

Design/methodology/approach

This paper focusses on an action research project developed by the authors, where the objective was to design and implement a dynamic performance measurement tool fitting the needs of department directors. The three authors were all involved in the project, respectively, as project manager, dashboard implementation manager and accounting manager of the studied organisation.

Findings

The results reveal a specific but complex change to the procedures and outcomes in the organisation studied, where the dashboard becomes a boundary infrastructure, thereby reviving technical and organisational problems that had been latent for years.

Originality/value

In this paper, the authors contribute to the debate on the digital age and the role of accounting with their exploration into the “revolution” of self-service BI tools. The democratisation and flexibility of these instruments put into discussion two core and somewhat controversial functions of accounting: data integration and personalised reporting.

Details

Journal of Accounting & Organizational Change, vol. 17 no. 1
Type: Research Article
ISSN: 1832-5912

Keywords

Content available
Article
Publication date: 14 July 2020

Salvatore V. Falletta and Wendy L. Combs

The purpose of the paper is to explore the meaning of Human Resources (HR) analytics and introduce the HR analytics cycle as a proactive and systematic process for…

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11030

Abstract

Purpose

The purpose of the paper is to explore the meaning of Human Resources (HR) analytics and introduce the HR analytics cycle as a proactive and systematic process for ethically gathering, analyzing, communicating and using evidence-based HR research and analytical insights to help organizations achieve their strategic objectives.

Design/methodology/approach

Conceptual review of the current state and meaning of HR analytics. Using the HR analytics cycle as a framework, the authors describe a seven-step process for building evidence-based and ethical HR analytics capabilities.

Findings

HR analytics is a nascent discipline and there are a multitude of monikers and competing definitions. With few exceptions, these definitions lack emphasis on evidence-based practice (i.e. the use of scientific research findings in adopting HR practices), ethical practice (i.e. ethically gathering and using HR data and insights) and the role of broader HR research and experimentation. More importantly, there are no practical models or frameworks available to help guide HR leaders and practitioners in doing HR analytics work.

Practical implications

The HR analytics cycle encompasses a broader range of HR analytics practices and data sources including HR research and experimentation in the context of social, behavioral and organizational science.

Originality/value

This paper introduces the HR analytics cycle as a practical seven-step approach for making HR analytics work in organizations.

Details

Journal of Work-Applied Management, vol. 13 no. 1
Type: Research Article
ISSN: 2205-2062

Keywords

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Article
Publication date: 2 March 2020

Ajax Persaud

This study aims to identify the precise competencies that employers are seeking for big data analytics professions and whether higher education big data programs enable…

Abstract

Purpose

This study aims to identify the precise competencies that employers are seeking for big data analytics professions and whether higher education big data programs enable students to acquire the competencies.

Design/methodology/approach

This study utilizes a multimethod approach involving three data sources: online job postings, executive interviews and big data programs at universities and colleges. Text mining analysis guided by a holistic competency theoretical framework was used to derive insights into the required competencies.

Findings

We found that employers are seeking workers with strong functional and cognitive competencies in data analytics, computing and business combined with a range of social competencies and specific personality traits. The exact combination of competencies required varies with job levels and tasks. Executives clearly indicate that workers rarely possess the competencies and they have to provide additional training.

Research limitations/implications

A limitation is our inability to capture workers' perspectives to determine the extent to which they think they have the necessary competencies.

Practical implications

The findings can be used by higher educational institutions to design programs to better meet market demand. Job seekers can use it to focus on the types of competencies they need to advance their careers. Policymakers can use it to focus policies and investments to alleviate skills shortages. Industry and universities can use it to strengthen their collaborations.

Social implications

Much closer collaborations among public institutions, educational institutions, industry, and community organizations are needed to ensure training programs evolve with the evolving need for skills driven by dynamic technological changes.

Originality/value

This is the first study on this topic to adopt a multimethod approach incorporating the perspectives of the key stakeholders in the supply and demand of skilled workers. It is the first to employ text mining analysis guided by a holistic competency framework to derive unique insights.

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