Data Science im HR-Management

2020 ◽  
pp. 115-132
Author(s):  
Melanie Baier
Keyword(s):  
2019 ◽  
Vol 61 (4) ◽  
pp. 15-42 ◽  
Author(s):  
Prasanna Tambe ◽  
Peter Cappelli ◽  
Valery Yakubovich

There is a substantial gap between the promise and reality of artificial intelligence in human resource (HR) management. This article identifies four challenges in using data science techniques for HR tasks: complexity of HR phenomena, constraints imposed by small data sets, accountability questions associated with fairness and other ethical and legal constraints, and possible adverse employee reactions to management decisions via data-based algorithms. It then proposes practical responses to these challenges based on three overlapping principles—causal reasoning, randomization and experiments, and employee contribution—that would be both economically efficient and socially appropriate for using data science in the management of employees.


Author(s):  
Charles Bouveyron ◽  
Gilles Celeux ◽  
T. Brendan Murphy ◽  
Adrian E. Raftery

Author(s):  
Shaveta Bhatia

 The epoch of the big data presents many opportunities for the development in the range of data science, biomedical research cyber security, and cloud computing. Nowadays the big data gained popularity.  It also invites many provocations and upshot in the security and privacy of the big data. There are various type of threats, attacks such as leakage of data, the third party tries to access, viruses and vulnerability that stand against the security of the big data. This paper will discuss about the security threats and their approximate method in the field of biomedical research, cyber security and cloud computing.


Author(s):  
Natalia V. Vysotskaya ◽  
T. V. Kyrbatskaya

The article is devoted to the consideration of the main directions of digital transformation of the transport industry in Russia. It is proposed in the process of digital transformation to integrate the community approach into the company's business model using blockchain technology and methods and results of data science; complement the new digital culture with a digital team and new communities that help management solve business problems; focus the attention of the company's management on its employees and develop those competencies in them that robots and artificial intelligence systems cannot implement: develop algorithmic, computable and non-linear thinking in all employees of the company.


Author(s):  
Serhii Kubitskyi ◽  
◽  
Oksana Chaika ◽  

This paper aims at considering the well-known triad of What? How? Why? somewhat anew by suggesting looking at transformational leadership for successful human resources management through the lens of coaching core competencies as the key soft skill. Arising as the strategic approach to the effective management of people, well-thought human resources management that rests on a leadership model definitely enables management of a company or organization to move ahead of the curve and gain a firm foothold in the job market. The transformational leadership model fits the framework of the research and links to the contrastive line between management and leadership.It is emphasized that management processes focus on (i) maintaining and (ii) improving performance at work, on the one hand, and on the other, unlike management, the transformational leadership model focuses on the benefits of visionary thinking and bringing about change. Following the goal in the subject matter associated with successful HR management, the Golden Circle of What? How? Why?introduced by Simon Sinek finds its way in the description analysis. The Why? sectionopens the idea for successful HR managementto move further to What?section and is accompanied with How? section in the end. The final part of the findings embodies 11 current core competencies of coaching, which illustrate how the ways of implementing the soft skills in workplace may increase HR performance, enhance seamless communication among employees and management, drive change and welcome innovation.The four objectives for successful HR management: (i) drive change within a company or organization, (ii) encourage and motivate people for personal and corporate growth and development, (iii) employ innovation including modern technologies, and (iv) lead by example, correspond to the four cornerstones in the framework for successful company or organization management via transformational leadership. They are: (i) create an inspire vision of the future for the company’s (organization’s) employees, (ii) motivate the staff to live by and deliver the vision, (iii) manage delivery of the vision, (iv) attract and retain high-class professionals and young talents, build up strong and competitive teams, create and grow ever-stronger, trust-based relationships with the employees. The toolkit of ways, techniques and approaches may derive from the current core competencies in coaching that can be groupedsimilarly to the ICF ones as follows: (i) foundation, (ii) co-creating the relationship, (iii) communicating effectively, and (iv) cultivating learning and growth.


2019 ◽  
Vol 5 (30) ◽  
pp. 960-968
Author(s):  
Güner Gözde KILIÇ
Keyword(s):  

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.


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