Comparative Analysis of Data Visualization Libraries Matplotlib and Seaborn in Python

2021 ◽  
Vol 10 (1) ◽  
pp. 277-281
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Programming Languages ◽  
Data Visualization ◽  
Data Science ◽  
Computational Modelling ◽  
And Mathematics ◽  
Python Programming ◽  
The Comparative Study

With the tremendous growth in the areas of computing, statistics, and mathematics has led to the rise of the emerging field of expertise, named ‘Data Science’. This paper focuses on the comparative study and evaluation of the data science libraries used in Python Programming Languages, named ‘Matplotlib’ and ‘Seaborn’. The sole purpose of this paper is to identify areas and evaluate the strengths and weaknesses of these libraries with the implementation of code and identify the classification of the univariate and multivariate plotting of data concerned with patterns of data visualization and computational modelling of data in the form of processed information using techniques of big data and data mining

scholarly journals CONTEMPORARY DATA SCIENCE FOR FINANCE STUDENTS. ESSENTIAL FEATURES OF COMMONLY USED STATISTICAL SOFTWARE - A COMPARATIVE STUDY

2019 ◽  
Vol 9 (2) ◽  
pp. 14-20
Author(s):  
Mădălina Viorica ION (MANU) ◽  
◽  
Ilie VASILE ◽  
Keyword(s):  
Comparative Study ◽  
Programming Languages ◽  
Data Science ◽  
Relevant Literature ◽  
Global Scale ◽  
Statistical Software ◽  
Business Excellence ◽  
Romanian Literature ◽  
Basic Features ◽  
Python Programming

This paper inventories some of the essential traits of the software preferred by researchers, students and professors, such as R or RStudio, or Matlab and also their possible utilizations. In order to fill the gap in the Romanian literature and help finance students in choosing proper tools according to the research purpose, this comparative study aims at bringing a fresh, useful perspective in the relevant literature. In Romania, the use of R was the focus of several international conferences on official statistics held in Bucharest, and others having business excellence, innovation and sustainability as purpose. In this time, at global scale, R and Python programming languages are considered the lingua franca of data science, as common statistical software used both in corporations and academia. In this paper, I analyze basic features of such software, with the purpose of application in finance.

Need for Dynamicity in Social Networking Sites

2014 ◽  
pp. 64-87
Author(s):  
Gurdeep S Hura
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Big Data ◽  
Social Networking ◽  
Real World ◽  
Social Networking Sites ◽  
Data Science ◽  
Emerging Technology ◽  
New Applications ◽  
Future Status

This chapter presents this new emerging technology of social media and networking with a detailed discussion on: basic definitions and applications, how this technology evolved in the last few years, the need for dynamicity under data mining environment. It also provides a comprehensive design and analysis of popular social networking media and sites available for the users. A brief discussion on the data mining methodologies for implementing the variety of new applications dealing with huge/big data in data science is presented. Further, an attempt is being made in this chapter to present a new emerging perspective of data mining methodologies with its dynamicity for social networking media and sites as a new trend and needed framework for dealing with huge amount of data for its collection, analysis and interpretation for a number of real world applications. A discussion will also be provided for the current and future status of data mining of social media and networking applications.

Visualization of Big Data Sets Using Computer Graphics

2018 ◽  
pp. 578-603
Author(s):  
Anna Ursyn ◽  
Edoardo L'Astorina
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Visual Analysis ◽  
Data Sets ◽  
Arrival Times ◽  
Learning And Teaching ◽  
Knowledge Domains ◽  
Visualization Of Data ◽  
And Mathematics ◽  
Visualization Techniques

This chapter discusses some possible ways of how professionals, researchers and users representing various knowledge domains are collecting and visualizing big data sets. First it describes communication through senses as a basis for visualization techniques, computational solutions for enhancing senses and ways of enhancing senses by technology. The next part discusses ideas behind visualization of data sets and ponders what is and what not visualization is. Further discussion relates to data visualization through art as visual solutions of science and mathematics related problems, documentation objects and events, and a testimony to thoughts, knowledge and meaning. Learning and teaching through data visualization is the concluding theme of the chapter. Edoardo L'Astorina provides visual analysis of best practices in visualization: An overlay of Google Maps that showed all the arrival times - in real time - of all the buses in your area based on your location and visual representation of all the Tweets in the world about TfL (Transport for London) tube lines to predict disruptions.

Big Data Analytics in Healthcare Sector

2019 ◽  
pp. 94-106 ◽  
Author(s):  
Sheik Abdullah A. ◽  
Selvakumar S. ◽  
Parkavi R. ◽  
Suganya R. ◽  
Abirami A. M.
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Implementation Process ◽  
Healthcare Sector ◽  
Data Preparation ◽  
Healthcare Data ◽  
R Programming ◽  
Python Programming

The importance of big data over analytics made the process of solving various real-world problems simpler. The big data and data science tool box provided a realm of data preparation, data analysis, implementation process, and solutions. Data connections over any data source, data preparation for analysis has been made simple with the availability of tremendous tools in data analytics package. Some of the analytical tools include R programming, python programming, rapid analytics, and weka. The patterns and the granularity over the observed data can be fetched with the visualizations and data observations. This chapter provides an insight regarding the types of analytics in a big data perspective with the realm in applicability towards healthcare data. Also, the processing paradigms and techniques can be clearly observed through the chapter contents.

Big Data Methods

2016 ◽  
Vol 21 (3) ◽  
pp. 525-547 ◽  
Author(s):  
Scott Tonidandel ◽  
Eden B. King ◽  
Jose M. Cortina
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Data Analytics ◽  
Data Science ◽  
Data Sources ◽  
Future Research ◽  
Organizational Science ◽  
Associated Data ◽  
Organizational Sciences

Advances in data science, such as data mining, data visualization, and machine learning, are extremely well-suited to address numerous questions in the organizational sciences given the explosion of available data. Despite these opportunities, few scholars in our field have discussed the specific ways in which the lens of our science should be brought to bear on the topic of big data and big data's reciprocal impact on our science. The purpose of this paper is to provide an overview of the big data phenomenon and its potential for impacting organizational science in both positive and negative ways. We identifying the biggest opportunities afforded by big data along with the biggest obstacles, and we discuss specifically how we think our methods will be most impacted by the data analytics movement. We also provide a list of resources to help interested readers incorporate big data methods into their existing research. Our hope is that we stimulate interest in big data, motivate future research using big data sources, and encourage the application of associated data science techniques more broadly in the organizational sciences.

scholarly journals Graph structures for data visualizations

2021 ◽  
Vol 6 (2) ◽  
pp. 24-31
Author(s):  
Stefana Janićijević ◽  
Vojkan Nikolić
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Data Types ◽  
Current State ◽  
Graph Structures ◽  
Big Data Visualization ◽  
Visualization Tools ◽  
Graph System ◽  
Data Visualizations

Networks are all around us. Graph structures are established in the core of every network system therefore it is assumed to be understood as graphs as data visualization objects. Those objects grow from abstract mathematical paradigms up to information insights and connection channels. Essential metrics in graphs were calculated such as degree centrality, closeness centrality, betweenness centrality and page rank centrality and in all of them describe communication inside the graph system. The main goal of this research is to look at the methods of visualization over the existing Big data and to present new approaches and solutions for the current state of Big data visualization. This paper provides a classification of existing data types, analytical methods, techniques and visualization tools, with special emphasis on researching the evolution of visualization methodology in recent years. Based on the obtained results, the shortcomings of the existing visualization methods can be noticed.

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 Pharmacoinformation studies of chondroprotectors

2021 ◽  
Vol 15 (5) ◽  
pp. 114-120
Author(s):  
A. M. Lila ◽  
I. Yu. Torshin ◽  
A. N. Gromov ◽  
V. A. Semenov ◽  
O. A. Gromova
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Chondroitin Sulfate ◽  
Data Selection ◽  
Scientific Publications ◽  
Modern Methods ◽  
Content Identification ◽  
New Biomarkers ◽  
Selection Of

The pharmacoinformation approach to the assessment and modeling of drugs involves the use of modern methods of data mining. These methods include: 1) analysis of big data (selection of texts of scientific publications, search for new biomarkers); 2) computer analysis of texts (automatic classification of texts by content, identification of pseudoscientific texts); 3) analysis of metric maps (visualization and analysis of complex patterns, including clustering) and 4) chemoinformation analysis, including the assessment of the effect of drugs on the transcriptome, proteome and microbiome of a person. The article provides examples of the application of these methods of pharmacoinformatics to chondroprotectors containing standardized forms of chondroitin sulfate and glucosamine sulfate.

Applying Big Data visualization to detect trends in 30 years of performance reports

Evaluation ◽  
2020 ◽  
Vol 26 (4) ◽  
pp. 516-540
Author(s):  
Eran Raveh ◽  
Yuval Ofek ◽  
Ron Bekkerman ◽  
Hertzel Cohen
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Data Science ◽  
Keyword Search ◽  
Mining Machine ◽  
Textual Information ◽  
Big Data Visualization ◽  
Performance Reports ◽  
User Friendly ◽  
Support Evaluation

Evaluators worldwide are dealing with a growing amount of unstructured electronic data, predominantly in textual format. Currently, evaluators analyze textual Big Data primarily using traditional content analysis methods based on keyword search, a practice that is limited to iterating over predefined concepts. But what if evaluators cannot define the necessary keywords for their analysis? Often we should examine trends in the way certain organizations have been operating, while our raw data are gigabytes of documents generated by that organization over decades. The problem is that in many cases we do not know what exactly we need to look for. In such cases, traditional analytical machinery would not provide an adequate solution within reasonable time—instead, heavy-lifting Big Data Science should be applied. We propose an automated, quantitative, user-friendly methodology based on text mining, machine learning, and data visualization, which assists researchers and evaluation practitioners to reveal trends, trajectories, and interrelations between bits and pieces of textual information in order to support evaluation. Our system automatically extracts a large amount of descriptive terminology for a particular domain in a given language, finds semantic connections between documents based on the extracted terminology, visualizes the entire document repository as a graph of semantic connections, and leads the user to the areas on that graph where most interesting trends can be observed. This article exemplifies the new method on 1700 performance reports, showing that the method can be used successfully, supplying evaluators with highly important information which cannot be revealed using other methods. Such exploratory exercise is vital as a preliminary exploratory phase for evaluations involving unstructured Big Data, after which a range of evaluation methods can be applied. We argue that our system can be successfully implemented on any domain evaluated.

Need for Dynamicity in Social Networking Sites

2013 ◽  
pp. 1-24 ◽  
Author(s):  
Gurdeep S Hura
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Big Data ◽  
Social Networking ◽  
Real World ◽  
Social Networking Sites ◽  
Data Science ◽  
Emerging Technology ◽  
New Applications ◽  
Future Status

This chapter presents this new emerging technology of social media and networking with a detailed discussion on: basic definitions and applications, how this technology evolved in the last few years, the need for dynamicity under data mining environment. It also provides a comprehensive design and analysis of popular social networking media and sites available for the users. A brief discussion on the data mining methodologies for implementing the variety of new applications dealing with huge/big data in data science is presented. Further, an attempt is being made in this chapter to present a new emerging perspective of data mining methodologies with its dynamicity for social networking media and sites as a new trend and needed framework for dealing with huge amount of data for its collection, analysis and interpretation for a number of real world applications. A discussion will also be provided for the current and future status of data mining of social media and networking applications.

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