scholarly journals PERBEDAAN DATA ENGINEER, DATA SCIENTIST DAN DATA ANALYST

Widya Accarya ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 306-309
Author(s):  
Ismail Setiawan
Keyword(s):  
Business Intelligence ◽  
Data Analyst ◽  
Level Data ◽  
Data Scientist

Seseorang yang ahli dalam keterampilan analisis data hanyalah keterampilan dasar seorang insinyur data. Keahlian statistik digunakan untuk memproses data baca dan tag, serta untuk mengkategorikan data. Karena erat kaitannya dengan pemodelan yang dibuat untuk menguji algoritma pada level data scientist. Model yang dibuat pada fase data scientist digunakan sebagai alat dalam fase business intelligence. Pada tahap akhir ini, eksekusi yang akan dilakukan harus memberikan dampak positif dan keuntungan yang besar bagi sebuah instansi.

Interactive Dashboard Design for Manager, Data Analyst and Data Scientist Perspective

2021 ◽  
Author(s):  
Temitope Olubunmi Awodiji
Keyword(s):  
Best Practices ◽  
Business Intelligence ◽  
Performance Indicators ◽  
Business Performance ◽  
Objective Assessment ◽  
Relevant Information ◽  
Good Decision ◽  
Data Analyst ◽  
Overall Performance ◽  
Data Scientist

With large amounts of unstructured data being produced every day, organizations are trying to extract as much relevant information as possible. This massive quantity of data is collected from a variety of sources, and data analysts and data scientists use it to create a dashboard that provides a complete picture of the organization's performance. Dashboards are business intelligence (BI) reporting tools that collect and show key metrics and key performance indicators (KPIs) on a single screen, enabling users to monitor and analyse business performance at a glance. An objective assessment of the company's overall performance, as well as of each department, is provided. If each department has access to the dashboard, it may serve as a springboard for future discussion and good decision-making. The goal of this article is to explain in detail the implementation of Dashboard and how it works, which will serve as a blueprint for building an effective dashboard with respect to best practices for dashboard design.

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.

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).

scholarly journals Inferring the dynamics of rising radical right-wing party support using Gaussian processes

2019 ◽  
Vol 377 (2160) ◽  
pp. 20190145 ◽  
Author(s):  
Björn R. H. Blomqvist ◽  
David J. T. Sumpter ◽  
Richard P. Mann
Keyword(s):  
Social Systems ◽  
Radical Right ◽  
Right Wing ◽  
Foreign Born ◽  
Dynamical Interaction ◽  
Data Analyst ◽  
Level Data ◽  
Education Levels ◽  
Regression Techniques ◽  
Party Support

The use of classical regression techniques in social science can prevent the discovery of complex, nonlinear mechanisms and often relies too heavily on both the expertise and prior expectations of the data analyst. In this paper, we present a regression methodology that combines the interpretability of traditional, well used, statistical methods with the full predictability and flexibility of Bayesian statistics techniques. Our modelling approach allows us to find and explain the mechanisms behind the rise of Radical Right-wing Populist parties (RRPs) that we would have been unable to find using traditional methods. Using Swedish municipality-level data (2002–2018), we find no evidence that the proportion of foreign-born residents is predictive of increases in RRP support. Instead, education levels and population density are the significant variables that impact the change in support for the RRP, in addition to spatial and temporal control variables. We argue that our methodology, which produces models with considerably better fit of the complexity and nonlinearities often found in social systems, provides a better tool for hypothesis testing and exploration of theories about RRPs and other social movements. This article is part of the theme issue ‘Coupling functions: dynamical interaction mechanisms in the physical, biological and social sciences’.

Skills and Competencies Required for Jobs in Business Analytics

2018 ◽  
pp. 880-904 ◽  
Author(s):  
Linda A. Leon ◽  
Kala Chand Seal ◽  
Zbigniew H. Przasnyski ◽  
Ian Wiedenman
Keyword(s):  
Content Analysis ◽  
Text Mining ◽  
Human Resources ◽  
Competitive Advantage ◽  
Career Planning ◽  
Business Analytics ◽  
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.

Game Data Science

2021 ◽  
Author(s):  
Magy Seif El-Nasr ◽  
Alessandro Canossa ◽  
Truong-Huy D. Nguyen ◽  
Anders Drachen
Keyword(s):  
Machine Learning ◽  
Case Studies ◽  
Data Science ◽  
Sequence Data ◽  
Decision Makers ◽  
User Research ◽  
Level Data ◽  
Methods And Techniques ◽  
The Subject ◽  
Data Scientist

This book is aimed at giving readers an introduction to the practical side of game data science and thus can be used a textbook for game analytics or game user research class or as a reference to self learners and enthusiasts. Game data science is a term that we use to denote a process composed of methods and techniques by which an analyst or a data scientist can make sense of data to allow decision makers in a game company to make informed decisions. This process involves: statistical analysis, visualization, abstraction of low-level data, machine learning and sequence data modeling. The book introduces different methods borrowing from different fields including human computer interaction, machine learning, and data science, focusing on methods and techniques used by both industry and researchers within the field of games. The book examples and case studies specifically focus on gameplay log data. The book takes a practical stance on the subject by discussing theoretical foundation, practical approaches, and delves deeply into the different techniques proposed and used through labs, examples, and comprehensive surveys of various case studies from both industry and academia. Topics range from simple approaches to more advanced ones. No prior knowledge is required. The book is developed to be self contained and can be used as a good way to introduce the reader to data science and how it is applied to the filed of games.

INFLUENCE OF BIG DATA ON THE DEVELOPMENT OF MANAGEMENT ACCOUNTING

2021 ◽  
Vol 4 (4) ◽  
pp. 107-110
Author(s):  
D. Yu. ROZHKOVA ◽  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Management Accounting ◽  
Big Data Analytics ◽  
Data Analyst ◽  
Analysis Methodology ◽  
Management Accountants ◽  
Wide Range ◽  
Information Products ◽  
Data Scientist

Continuous advances in artificial intelligence and machine learning, combined with big data analytics, increase the risk of computerization and the disappearance of a wide range of professionals, including accountants and management accountants. The article discusses the fact of emergence of data scientist specialists, as well as the prospects for the development of data analysis functions. Based on a data analysis methodology that takes into account the emergence of big data, we positioned management accounting specialists and data analysts based on their functionality and data used. It was concluded that today data analyst functionality complements the skills and knowledge of accountants. However, the further vector of development will depend on the needs of an enterprise and development of analytical methods and information products.

Insider-Threat Detection in Corporate Espionage and Cyber-Espionage

2016 ◽  
pp. 62-77
Author(s):  
Kirk Y Williams
Keyword(s):  
Detection Methods ◽  
Insider Threat ◽  
Threat Detection ◽  
Governmental Agency ◽  
Education Access ◽  
Governmental Agencies ◽  
Data Analyst ◽  
External Threat ◽  
Cyber Espionage ◽  
Data Scientist

National Security will always be threatened by individuals internal to the organization in the form of an insider-threat and external to the organization in the form of corporate espionage or cyber-espionage. Therefore, insider-threat detection methods, security precautions, authentication processes, and standard operating procedures for employees should be in place to try to reduce the instances of an insider-threat and/or an external threat breaching the security of an organization, institution, company, or governmental agency. Espionage and cyber-espionage can and does occur; however, it is not usually made public knowledge and when it does, it can have grave effects on the organization, institution, company, or governmental agency in which it occurred. Within this chapter the author explores how an insider-threat in the form of a Data Scientist, Penetration Tester, or Data Analyst can use their education, access, and background to gain access to systems and information that can be of value to external organizations, institutions, companies, and/or governmental agencies.

New Evidence on the Construct Validity of the ASI-3 and the Dimensional Conceptualization of Trait Anxiety Sensitivity From IRT Modeling

2017 ◽  
Vol 33 (3) ◽  
pp. 181-189 ◽  
Author(s):  
Christoph J. Kemper ◽  
Michael Hock
Keyword(s):  
Construct Validity ◽  
Anxiety Sensitivity ◽  
Latent Class ◽  
Dimensional Structure ◽  
Sensitivity Index ◽  
Response Scale ◽  
Established Risk Factor ◽  
Irt Models ◽  
Level Data ◽  
Extreme Response

Abstract. Anxiety Sensitivity (AS) denotes the tendency to fear anxiety-related sensations. Trait AS is an established risk factor for anxiety pathology. The Anxiety Sensitivity Index-3 (ASI-3) is a widely used measure of AS and its three most robust dimensions with well-established construct validity. At present, the dimensional conceptualization of AS, and thus, the construct validity of the ASI-3 is challenged. A latent class structure with two distinct and qualitatively different forms, an adaptive form (normative AS) and a maladaptive form (AS taxon, predisposing for anxiety pathology) was postulated. Item Response Theory (IRT) models were applied to item-level data of the ASI-3 in an attempt to replicate previous findings in a large nonclinical sample (N = 2,603) and to examine possible interpretations for the latent discontinuity observed. Two latent classes with a pattern of distinct responses to ASI-3 items were found. However, classes were indicative of participant’s differential use of the response scale (midpoint and extreme response style) rather than differing in AS content (adaptive and maladaptive AS forms). A dimensional structure of AS and the construct validity of the ASI-3 was supported.

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