Controller versus Data Scientist: Welche Berufsgruppe überlebt den digitalen Wandel?

2021 ◽  
pp. 17-40
Author(s):  
Klaus-Peter Schoeneberg ◽  
Christian von Schudnat ◽  
Erkan Gürbüz
Keyword(s):  
Versus Data ◽  
Data Scientist

scholarly journals A Conceptual Model to Support the Transmut-ers in Acquiring the Desired Knowledge of a Data Scientist

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

scholarly journals Assumption‐versus data‐based approaches to summarizing species’ ranges

2016 ◽  
Vol 32 (3) ◽  
pp. 568-575 ◽  
Author(s):  
A. Townsend Peterson ◽  
Adolfo G. Navarro‐Sigüenza ◽  
Alejandro Gordillo
Keyword(s):  
Species Ranges ◽  
Versus Data

The science of statistics versus data science: What is the future?

2021 ◽  
Vol 173 ◽  
pp. 121111
Author(s):  
Hossein Hassani ◽  
Christina Beneki ◽  
Emmanuel Sirimal Silva ◽  
Nicolas Vandeput ◽  
Dag Øivind Madsen
Keyword(s):  
Data Science ◽  
Versus Data ◽  
The Future

Big Data and Analytics

2017 ◽  
Vol 8 (3) ◽  
pp. 79-92 ◽  
Author(s):  
Dominik Krimpmann ◽  
Anna Stühmeier
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Quantitative Analysis ◽  
Corporate World ◽  
Business Results ◽  
Key Concepts ◽  
Data Scientist

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.

Skills and Competencies Required for Jobs in Business Analytics

2017 ◽  
Vol 8 (1) ◽  
pp. 1-25 ◽  
Author(s):  
Linda A. Leon ◽  
Kala Chand Seal ◽  
Zbigniew H. Przasnyski ◽  
Ian Wiedenman
Keyword(s):  
Content Analysis ◽  
Human Resources ◽  
Data Analytics ◽  
Career Planning ◽  
Business Analytics ◽  
High Demand ◽  
Data Analyst ◽  
Job Advertisements ◽  
Data Scientist ◽  
Business Analyst

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.

Models Versus Data

2017 ◽  
pp. 85-96
Author(s):  
Salsburg David
Keyword(s):  
Versus Data

scholarly journals What is a Data Scientist? Analysis of core soft and technical competencies in job postings

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

scholarly journals Data Wrangling using Python

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3491-3495
Keyword(s):  
Data Analytics ◽  
Data Science ◽  
Research Work ◽  
Research Area ◽  
Mapping Data ◽  
Data Format ◽  
Data Analyst ◽  
Data Engineering ◽  
Data Scientist ◽  
Term Data

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).

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