Data Science is Here

2020 ◽  
pp. 108-127
Author(s):  
Dimitar Grozdanov Christozov ◽  
Katia Rasheva-Yordanova ◽  
Stefka Toleva-Stoimenova
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Science ◽  
Informed Decision Making ◽  
Knowledge And Skills ◽  
Analytical Skills ◽  
Analytical Thinking ◽  
Data Scientist ◽  
Primary Focus ◽  
Science Training

With the advent of big data, the search for respective data experts has become more intensive. This study aims to discuss data scientist skills and some topical issues that are related to data specialist profiles. A complex competence model has been deployed, dividing the skills into three groups: hard, soft, and analytical skills. The primary focus is on analytical thinking as one of the key competences of the successful data scientist taking into account the trans-discipline nature of data science. The chapter considers a new digital divide between the society and this small group of people that make sense out of the vast data and help the organization in informed decision making. As data science training needs to be business-oriented, the curricula of the Master's degree in Data Science is compared with the required knowledge and skills for recruitment.

scholarly journals APPLICATION AND PLATFORM DESIGN OF GEOSPATIAL BIG DATA

2021 ◽  
Vol XLIII-B4-2021 ◽  
pp. 293-300
Author(s):  
H. Li ◽  
W. Huang ◽  
Z. Zha ◽  
J. Yang
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Resource Sharing ◽  
Data Science ◽  
Geographic Information ◽  
Management Decision ◽  
Functional Requirements ◽  
Comprehensive Service ◽  
Data Platform ◽  
The Impact

Abstract. With the wide application of Big Data, Artificial Intelligence and Internet of Things in geographic information technology and industry, geospatial big data arises at the historic moment. In addition to the traditional "5V" characteristics of big data, which are Volume, Velocity, Variety, Veracity and Valuable, geospatial big data also has the characteristics of "Location Attribute". At present, the study of geospatial big data are mainly concentrated in: knowledge mining and discovery of geospatial data, Spatiotemporal big data mining, the impact of geospatial big data on visualization, social perception and smart city, geospatial big data services for government decision-making support four aspects. Based on the connotation and extension of geospatial big data, this paper comprehensively defines geospatial big data comprehensively. The application of geospatial big data in location visualization, industrial thematic geographic information comprehensive service and geographic data science and knowledge service is introduced in detail. Furthermore, the key technologies and design indicators of the National Geospatial Big Data Platform are elaborated from the perspectives of infrastructure, functional requirements and non-functional requirements, and the design and application of the National Geospatial Public Service Big Data Platform are illustrated. The challenges and opportunities of geospatial big data are discussed from the perspectives of open resource sharing, management decision support and data security. Finally, the development trend and direction of geospatial big data are summarized and prospected, so as to build a high-quality geospatial big data platform and play a greater role in social public application services and administrative management decision-making.

scholarly journals Visualização de dados: passado, presente e futuro | Data vizualization: past, present and future

2019 ◽  
Vol 15 (2) ◽  
Author(s):  
Fabiano Couto Corrêa da Silva
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Data Science ◽  
Multivariate Data ◽  
Scientific Data ◽  
Cutting Edge ◽  
Cognitive Analysis ◽  
Analytical Thinking ◽  
Over Time

RESUMO São expostos os princípios fundamentais da ciência de dados e as generalidades de uma de suas áreas de estudo: a Visualização de dados. O artigo aborda como os dados multivariados tem sido representados por meio de imagens e gráficos ilustrados que relacionam os elementos de sintaxe e semântica que podem contemplar o pensamento analítico nas margens visuais. Analisa como a Visualização de Dados foi desenvolvida ao longo do tempo, utilizando exemplos reconhecidos como de vanguarda neste campo, validando a pesquisa com análise cognitivas básicas em princípios de apresentação de evidências nos displays de informação.Palavras-chave: Visualização de Dados; Infografias; Dados Científicos; Storytelling, Big Data.ABSTRACT The fundamental principles of data science and the generalities of one of its areas of study are exposed: Data Visualization. The article discusses how multivariate data has been represented through illustrated images and graphs that relate the elements of syntax and semantics that can include analytical thinking in visual margins. It analyzes how Data Visualization has been developed over time, using examples recognized as cutting edge in this field, validating research with basic cognitive analysis on principles of evidence presentation in information displays.Keywords: Data Visualization; Infographics; Scientific Data; Storytelling, Big Data.

scholarly journals Cooking Up Knowledge From Big Data Using Data Science

2021 ◽  
Vol 9 ◽  
Author(s):  
Andrea Rau
Keyword(s):  
Big Data ◽  
Computer Science ◽  
Programming Language ◽  
Data Science ◽  
Weather Forecasting ◽  
Original Question ◽  
Data Scientist ◽  
Using Data ◽  
The Way

Data collected in very large quantities are called big data, and big data has changed the way we think about and answer questions in many different fields, like weather forecasting and biology. With all this information available, we need computers to help us store, process, analyze, and understand it. Data science combines tools from fields like statistics, mathematics, and computer science to find interesting patterns in big data. Data scientists write step-by-step instructions called algorithms to teach computers how to learn from data. To help computers understand these instructions, algorithms must be translated from the original question asked by a data scientist into a programming language—and the results must be translated back, so that humans can understand them. That means that data scientists are data detectives, programmers, and translators all in one!

scholarly journals Enablers to supply chain performance on the basis of digitization technologies

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Himanshu Gupta ◽  
Sarangdhar Kumar ◽  
Simonov Kusi-Sarpong ◽  
Charbel Jose Chiappetta Jabbour ◽  
Martin Agyemang
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Supply Chain ◽  
Information Technologies ◽  
Data Science ◽  
Content Type ◽  
Big Data Technologies ◽  
Fierce Competition ◽  
Practical Implications ◽  
Improve Supply Chain

PurposeThe aim of this study is to identify and prioritize a list of key digitization enablers that can improve supply chain management (SCM). SCM is an important driver for organization's competitive advantage. The fierce competition in the market has forced companies to look the past conventional decision-making process, which is based on intuition and previous experience. The swift evolution of information technologies (ITs) and digitization tools has changed the scenario for many industries, including those involved in SCM.Design/methodology/approachThe Best Worst Method (BWM) has been applied to evaluate, rank and prioritize the key digitization and IT enablers beneficial for the improvement of SC performance. The study also used additive value function to rank the organizations on their SC performance with respect to digitization enablers.FindingsThe total of 25 key enablers have been identified and ranked. The results revealed that “big data/data science skills”, “tracking and localization of products” and “appropriate and feasibility study for aiding the selection and adoption of big data technologies and techniques ” are the top three digitization and IT enablers that organizations need to focus much in order to improve their SC performance. The study also ranked the SC performance of the organizations based on digitization enablers.Practical implicationsThe findings of this study will help the organizations to focus on certain digitization technologies in order to improve their SC performance. This study also provides an original framework for organizations to rank the key digitization enablers according to enablers relevant in their context and also to compare their performance with their counterparts.Originality/valueThis study seems to be the first of its kind in which 25 digitization enablers categorized in four main categories are ranked using a multi-criteria decision-making (MCDM) tool. This study is also first of its kind in ranking the organizations in their SC performance based on weights/ranks of digitization enablers.

Intelligent Big Data Analytics

2019 ◽  
pp. 1-19 ◽  
Author(s):  
Zhaohao Sun
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Business Decision ◽  
Technology System ◽  
System Service ◽  
Managerial Perspective

Intelligent big data analytics is an emerging paradigm in the age of big data, analytics, and artificial intelligence (AI). This chapter explores intelligent big data analytics from a managerial perspective. More specifically, it first looks at the age of trinity and argues that intelligent big data analytics is at the center of the age of trinity. This chapter then proposes a managerial framework of intelligent big data analytics, which consists of intelligent big data analytics as a science, technology, system, service, and management for improving business decision making. Then it examines intelligent big data analytics for management taking into account four managerial functions: planning, organizing, leading, and controlling. The proposed approach in this chapter might facilitate the research and development of intelligent big data analytics, big data analytics, business intelligence, artificial intelligence, and data science.

scholarly journals Data Analytics in Banking and Financial Services

2019 ◽  
Vol 8 (S1) ◽  
pp. 67-69
Author(s):  
S. Palaniammal ◽  
V. S. Thangamani
Keyword(s):  
Decision Making ◽  
Developing Countries ◽  
Big Data ◽  
Financial Services ◽  
Data Analytics ◽  
Data Science ◽  
Banking Industry ◽  
Rapid Development ◽  
Banking Services ◽  
The Impact

In Journal of Banking and Finance [1] we are living in the era of the big data. The rapid development of scientific and data technology over the past decade has brought not only new and sophisticated analytical tools into Financial and Banking services, but also introduced the power of data science application in everyday strategic and operational management. Data analytics and science developments have been particularly valuable to financial organizations that heavily depend on financial information in their decision making processes. The article presents the research that focuses on the impact of the data and technology trends on decision making, particularly in Finance and Banking services. It covers an overview of the benefits associated with the decision analytics and the use of big data by financial organizations. The aim of the research is to highlight the areas of impact where the big data trends are creating disruptive changes to the way the Finance and banking industry traditionally operates. For example, we can see rapid changes to organisation structures, approach to competition and customer as well as the recognition of the importance of data analytics in strategic and tactical decision making. Investment in data analytics is no longer considered a luxury, but necessity, especially for the financial organizations in developing countries. Technology and data science are both forcing and enabling the financial and banking industry to respond to transformative demands and adapt to rapidly changing market conditions in order to survive and thrive in highly competitive global environment. Financial companies operating in developing countries must develop strong understanding of data-related trends and impacts as well as opportunities. This knowledge should not only be utilized for survival efforts, but also seen as the opportunity to engage at global level through innovation, flexibility, and early adoption of data science benefits. The paper also recommends further studies in related areas, which would provide additional value and awareness to the organizations that are considering their participation in the global data and analytical trends.

scholarly journals Mining Big Data in Education: Affordances and Challenges

2020 ◽  
Vol 44 (1) ◽  
pp. 130-160 ◽  
Author(s):  
Christian Fischer ◽  
Zachary A. Pardos ◽  
Ryan Shaun Baker ◽  
Joseph Jay Williams ◽  
Padhraic Smyth ◽  
...  
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Language Processing ◽  
Data Privacy ◽  
Data Science ◽  
Research Training ◽  
Cognitive Strategies ◽  
Early Warning Systems ◽  
Clickstream Data ◽  
Institutional Data

The emergence of big data in educational contexts has led to new data-driven approaches to support informed decision making and efforts to improve educational effectiveness. Digital traces of student behavior promise more scalable and finer-grained understanding and support of learning processes, which were previously too costly to obtain with traditional data sources and methodologies. This synthetic review describes the affordances and applications of microlevel (e.g., clickstream data), mesolevel (e.g., text data), and macrolevel (e.g., institutional data) big data. For instance, clickstream data are often used to operationalize and understand knowledge, cognitive strategies, and behavioral processes in order to personalize and enhance instruction and learning. Corpora of student writing are often analyzed with natural language processing techniques to relate linguistic features to cognitive, social, behavioral, and affective processes. Institutional data are often used to improve student and administrational decision making through course guidance systems and early-warning systems. Furthermore, this chapter outlines current challenges of accessing, analyzing, and using big data. Such challenges include balancing data privacy and protection with data sharing and research, training researchers in educational data science methodologies, and navigating the tensions between explanation and prediction. We argue that addressing these challenges is worthwhile given the potential benefits of mining big data in education.

scholarly journals The role of data science in healthcare advancements: applications, benefits, and future prospects

2021 ◽  
Author(s):  
Sri Venkat Gunturi Subrahmanya ◽  
Dasharathraj K. Shetty ◽  
Vathsala Patil ◽  
B. M. Zeeshan Hameed ◽  
Rahul Paul ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Unstructured Data ◽  
Healthcare Applications ◽  
Interdisciplinary Field

AbstractData science is an interdisciplinary field that extracts knowledge and insights from many structural and unstructured data, using scientific methods, data mining techniques, machine-learning algorithms, and big data. The healthcare industry generates large datasets of useful information on patient demography, treatment plans, results of medical examinations, insurance, etc. The data collected from the Internet of Things (IoT) devices attract the attention of data scientists. Data science provides aid to process, manage, analyze, and assimilate the large quantities of fragmented, structured, and unstructured data created by healthcare systems. This data requires effective management and analysis to acquire factual results. The process of data cleansing, data mining, data preparation, and data analysis used in healthcare applications is reviewed and discussed in the article. The article provides an insight into the status and prospects of big data analytics in healthcare, highlights the advantages, describes the frameworks and techniques used, briefs about the challenges faced currently, and discusses viable solutions. Data science and big data analytics can provide practical insights and aid in the decision-making of strategic decisions concerning the health system. It helps build a comprehensive view of patients, consumers, and clinicians. Data-driven decision-making opens up new possibilities to boost healthcare quality.

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