The Data Scientist on LinkedIn: Job Advertisement Corpus Processing with NooJ

Author(s):  
Maddalena della Volpe ◽  
Francesca Esposito
2018 ◽  
Vol 110 (1) ◽  
pp. 85-101 ◽  
Author(s):  
Ronald Cardenas ◽  
Kevin Bello ◽  
Alberto Coronado ◽  
Elizabeth Villota

Abstract Managing large collections of documents is an important problem for many areas of science, industry, and culture. Probabilistic topic modeling offers a promising solution. Topic modeling is an unsupervised machine learning method and the evaluation of this model is an interesting problem on its own. Topic interpretability measures have been developed in recent years as a more natural option for topic quality evaluation, emulating human perception of coherence with word sets correlation scores. In this paper, we show experimental evidence of the improvement of topic coherence score by restricting the training corpus to that of relevant information in the document obtained by Entity Recognition. We experiment with job advertisement data and find that with this approach topic models improve interpretability in about 40 percentage points on average. Our analysis reveals as well that using the extracted text chunks, some redundant topics are joined while others are split into more skill-specific topics. Fine-grained topics observed in models using the whole text are preserved.


IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Muhammad Javed Ramzan ◽  
Saif Ur Rehman Khan ◽  
Inayat Ur-Rehman ◽  
Tamim Ahmed Khan ◽  
Adnan Akhunzada ◽  
...  

2016 ◽  
Vol 12 (3) ◽  
pp. 484-505 ◽  
Author(s):  
Joana Story ◽  
Filipa Castanheira ◽  
Silvia Hartig

Purpose Talent management is a twenty-first-century concern. Attracting talented individuals to organizations is an important source for firm competitive advantage. Building on signaling theory, this paper proposes that corporate social responsibility (CSR) can be an important tool for talent recruitment. Design/methodology/approach Across two studies, this paper found support for this hypothesized relationship. In Study 1, a job advertisement was manipulated to include information about CSR and tested it in two groups of 120 master’s degree students who would be in the job market within the year. It was found that CSR was an important factor that increased organizational attractiveness. In Study 2, with 532 external talented stakeholders of 16 organizations, our findings were replicated and advanced by testing whether perceptions of CSR practices (internal and external) influenced perceptions of organizational attractiveness and if this relationship was mediated by organizational reputation. Findings This study found that perceptions of internal CSR practices were directly related to both organizational attractiveness and firm reputation. However, perceptions of external CSR practices were related only to organizational attractiveness through organizational reputation. Research limitations/implications The article’s one of the main limitations has to do with generalizability of the results and the potential common method variance bias. Practical implications The findings demonstrate that CSR can play an effective role in attracting potential employees, through enhancement of organizational reputation and organizational attractiveness. If organizations are willing to implement practices that protect and develop their employees, along with practices that improve the quality of the natural environment and the well-being of the society, they can become an employer-of-choice. Originality/value This study expands on previous studies by including an experimental design, including two types of CSR practices and a mediating variable in this field study.


Author(s):  
Dominik Krimpmann ◽  
Anna Stühmeier

Big Data and Analytics have become key concepts within the corporate world, both commercially and from an information technology (IT) perspective. This paper presents the results of a global quantitative analysis of 400 IT leaders from different industries, which examined their attitudes toward dedicated roles for an Information Architect and a Data Scientist. The results illustrate the importance of these roles at the intersection of business and technology. They also show that to build sustainable and quantifiable business results and define an organization's competitive positioning, both roles need to be dedicated, rather than shared across different people. The research also showed that those dedicated roles contribute actively to a sustainable competitive positioning mainly driven by visualization of complex matters.


2017 ◽  
Vol 8 (1) ◽  
pp. 1-25 ◽  
Author(s):  
Linda A. Leon ◽  
Kala Chand Seal ◽  
Zbigniew H. Przasnyski ◽  
Ian Wiedenman

The explosive growth of business analytics has created a high demand for individuals who can help organizations gain competitive advantage by extracting business knowledge from data. What types of jobs satisfy this demand and what types of skills should individuals possess to satisfy this huge and growing demand? The authors perform a content analysis of 958 job advertisements posted during 2014-2015 for four types of positions: business analyst, data analyst, data scientist, and data analytics manager. They use a text mining approach to identify the skills needed for these job types and identify six distinct broad competencies. They also identify the competencies unique to a particular type of job and those common to all job types. Their job type categorization provides a framework that organizations can use to inventory their existing workforce competencies in order to identify critical future human resources. It can also guide individual professionals with their career planning as well as academic institutions in assessing and advancing their business analytics curricula.


2020 ◽  
Vol 8 (1) ◽  
pp. 25-39
Author(s):  
Carolina Coelho da Silveira ◽  
Carla Bonato Marcolin ◽  
Matheus Da Silva ◽  
Jean Carlos Domingos

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3491-3495

The term Data Engineering did not get much popularity as the terminologies like Data Science or Data Analytics, mainly because the importance of this technique or concept is normally observed or experienced only during working with data or handling data or playing with data as a Data Scientist or Data Analyst. Though neither of these two, but as an academician and the urge to learn, while working with Python, this topic ‘Data engineering’ and one of its major sub topic or concept ‘Data Wrangling’ has drawn attention and this paper is a small step to explain the experience of handling data which uses Wrangling concept, using Python. So Data Wrangling, earlier referred to as Data Munging (when done by hand or manually), is the method of transforming and mapping data from one available data format into another format with the idea of making it more appropriate and important for a variety of relatedm purposes such as analytics. Data wrangling is the modern name used for data pre-processing rather Munging. The Python Library used for the research work shown here is called Pandas. Though the major Research Area is ‘Application of Data Analytics on Academic Data using Python’, this paper focuses on a small preliminary topic of the mentioned research work named Data wrangling using Python (Pandas Library).


2006 ◽  
Author(s):  
Daniel C. Feldman ◽  
William O. Bearden ◽  
David M. Hardesty
Keyword(s):  

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