scholarly journals VISUALIZATION OF E-COMMERCE DATA USING AUGMENTED REALITY

2020 ◽  
Vol 5 (19) ◽  
pp. 104-122
Author(s):  
Azzan Amin ◽  
Haslina Arshad ◽  
Ummul Hanan Mohamad
Keyword(s):  
Machine Learning ◽  
Augmented Reality ◽  
Data Visualization ◽  
Data Science ◽  
Moving Average ◽  
Open Data ◽  
Visual Presentation ◽  
Depth Dimension ◽  
2D Data ◽  
Visualization Application

Data visualization is viewed as a significant element in data analysis and communication. As the data engagement becomes more and more complex, visual presentation of data does help users understand the data. So far, two-dimensional (2D) data visuals are often used for the data visualization process, but the lack of depth dimension leads to inefficient and limited understanding of the data. Therefore, the effectiveness of augmented reality (AR) in data visualization was studied through the development of an AR Data Visualization application using E-commerce data. Machine learning models are also involved in the development of this AR application for the provision of data using predictive analysis functions. To provide quality E-commerce data and an optimal machine learning model, the data science process is carried out using the python programming language. The E-commerce data selected for this study is open data taken through the Kaggle Website. This database has 9994 data numbers and 21 attributes. This AR data visualization application will make it easier for users to understand the E-commerce data in-depth through the use of AR technology and be able to visualize the forecasts for sales profit based on the algorithm model "Auto-Regressive Integrated Moving Average" (ARIMA).

Data science skills: Building partnership for efficient school curriculum delivery in Africa

2020 ◽  
Vol 36 ◽  
pp. 49-62
Author(s):  
Nureni Olawale Adeboye ◽  
Peter Osuolale Popoola ◽  
Oluwatobi Nurudeen Ogunnusi
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Open Data ◽  
School Curriculum ◽  
Global Perspective ◽  
Special Focus ◽  
African Countries ◽  
Tertiary Institutions ◽  
Data Description ◽  
Curriculum Delivery

Data science is a concept to unify statistics, data analysis, machine learning and their related methods in order to analyze actual phenomena with data to provide better understanding. This article focused its investigation on acquisition of data science skills in building partnership for efficient school curriculum delivery in Africa, especially in the area of teaching statistics courses at the beginners’ level in tertiary institutions. Illustrations were made using Big data of selected 18 African countries sourced from United Nations Educational, Scientific and Cultural Organization (UNESCO) with special focus on some macro-economic variables that drives economic policy. Data description techniques were adopted in the analysis of the sourced open data with the aid of R analytics software for data science, as improvement on the traditional methods of data description for learning and thus open a new charter of education curriculum delivery in African schools. Though, the collaboration is not without its own challenges, its prospects in creating self-driven learning culture among students of tertiary institutions has greatly enhanced the quality of teaching, advancing students skills in machine learning, improved understanding of the role of data in global perspective and being able to critique claims based on data.

scholarly journals O impacto do uso de ferramenta de visualização de dados abertos como recurso de ensino e aprendizagem / The impact of use open data visualization tools how teaching and learning resource

2015 ◽  
Vol 25 (44) ◽  
pp. 97-117
Author(s):  
Alex Alex Sander Miranda Lobo ◽  
Luiz Claudio Gomes Maia ◽  
Fernando Silva Parreiras
Keyword(s):  
Information And Communication Technologies ◽  
Data Visualization ◽  
Teaching And Learning ◽  
Open Data ◽  
Communication Technologies ◽  
Use Of Technology ◽  
Teachers And Students ◽  
Information And Communication ◽  
The Impact ◽  
Visualization Application

Este artigo apresenta uma pesquisa de dissertação, na qual se buscou desenvolver uma ferramenta de visualização de Dados Abertos (Open Data) para uso no processo de ensino e aprendizagem em uma turma do terceiro ano do Ensino Médio na disciplina de Geografia. Teve como objetivo principal verificar como essa ferramenta influenciaria nesse processo. Para atingir o objetivo do trabalho, foi realizada uma pesquisa preponderantemente qualitativa com natureza descritiva, com referencial teórico baseado na aprendizagem significativa e no uso das tecnologias da informação e comunicação no processo de ensino e aprendizagem. Foi realizada uma entrevista inicial junto ao professor da disciplina e a aplicação de questionários ao professor e aos alunos do terceiro ano, após o uso da aplicação de visualização de dados abertos e, por fim, foi proposto um teste avaliativo entre turmas que usaram o aplicativo e turmas que não o usaram. Na análise dos resultados, concluiu-se que a ferramenta trouxe vários aspectos positivos no processo de ensino e aprendizagem, como uma atenção maior por parte dos alunos em relação ao conteúdo, uma motivação a mais no processo de ensino e aprendizagem, tendo apresentado aspectos relacionados à aprendizagem significativa e mostrado que os alunos que fizeram o uso da aplicação tiveram um melhor desempenho em relação aos que não fizeram o uso da tecnologia no conteúdo proposto na disciplina.Palavras-Chave: Educação. Dados Abertos. Ensino e AprendizagemAbstractThis article presents a research dissertation, which aimed to develop a visualization tool of Open Data (Open Data) for use in the process of teaching and learning in a class of third year of high school in geography discipline. Aimed to assess how these influence tool in this process. To achieve the goal of the work, mainly qualitative research was conducted with descriptive, and the theoretical framework based on meaningful learning and the use of information and communication technologies in teaching and learning. An initial interview was conducted with the subject teacher and the application of questionnaires to teachers and students of the third year after the use of open data visualization application and, finally, an evaluation test between groups who used the application was proposed and classes than used. In analyzing the results, it was concluded that the tool has brought many positive aspects in the process of teaching and learning, such as greater attention from students regarding the content, one more motivation in the process of teaching and learning, presenting aspects the significant learning and shown that students who have made the use of the application performed better than those who did not make the use of technology in the proposed content of the discipline.Keywords: Education. Open Data. Teaching and Learning. 

scholarly journals Data Visualization on Crime Against Women

2019 ◽  
Vol 8 (2S4) ◽  
pp. 83-85
Keyword(s):  
Machine Learning ◽  
Data Visualization ◽  
Data Science ◽  
Learning Algorithm ◽  
Current Data ◽  
Machine Learning Algorithm ◽  
Graphical Approach ◽  
Crime Data ◽  
Statistical Variance

Visualization ensures the modern expectation of all forms of data. It is important to understand the data and its statistical variance graphically. Visualization on crime data would be supportive to analyze and prevent the threats in society. According to recent surveys and records, India has undergone many crime issues which occur on women. In order to prevent and analyze the crime issues against women, Data visualization is a useful approach to deal with it. The current data technologies available are appropriate to accomplish the task of visualization for women safety. Efficient visualization with effective machine learning algorithm and its performance finds the response for data related requests in the field of data science. This paper clarifies the details of crime against women through a graphical approach and illustrates about how to notify the unsafe levels by alert to safeguard the women

scholarly journals A Study of Real World Data Visualization of COVID-19 dataset using Python

2020 ◽  
Vol 4 (8) ◽  
pp. 104-107
Keyword(s):  
Machine Learning ◽  
Data Visualization ◽  
Real World ◽  
Data Science ◽  
Real World Data ◽  
World Data ◽  
Corona Virus ◽  
To Come

The importance of data science and machine learning is evident in all the domains where any kind of data is generated. The multi aspect analysis and visualizations help the society to come up with useful solutions and formulate policies. This paper takes the live data of current pandemic of Corona Virus and presents multi-faceted views of the data as to help the authorities and Governments to take appropriate decisions to takle this unprecedented problem. Python and its libraries along with Google Colab platform is used to get the results. The best possible techniques and combinations of modules/libraries are used to present the information related to COVID-19..

scholarly journals Data Journalism: From Social Science Techniques to Data Science Skills

Hipertext.net ◽  
2020 ◽  
pp. 41-54
Author(s):  
Adolfo Antón Bravo ◽  
Ana Serrano Tellería
Keyword(s):  
Social Science ◽  
Data Visualization ◽  
Data Science ◽  
Open Data ◽  
Structured Data ◽  
Scientific Methods ◽  
Data Literacy ◽  
Data Journalism ◽  
Source Of Information

In very few years in journalism, we have gone from looking at social science techniques, what was called precision journalism, to dealing with open data as a huge source of information that lead us to data journalism what connects with data science in the sense of using -again- scientific methods to extract knowledge and insights from structured data. This article offers an overview of that evolution and focuses on some prototypes that have emerged in this new journalistic ecosystem of data journalism, data visualization and data literacy.

scholarly journals Big Data Analytics in Australian Local Government

Smart Cities ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 657-675
Author(s):  
Richard B. Watson ◽  
Peter J. Ryan
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Local Governments ◽  
Data Science ◽  
Smart Cities ◽  
Open Data ◽  
Big Data Analytics ◽  
State Level ◽  
Local Council ◽  
Open Datasets

Australian governments at all three levels—local (council), state, and federal—are beginning to exploit the massive amounts of data they collect through sensors and recording systems. Their aim is to enable Australian communities to benefit from “smart city” initiatives by providing greater efficiencies in their operations and strategic planning. Increasing numbers of datasets are being made freely available to the public. These so-called big data are amenable to data science analysis techniques including machine learning. While there are many cases of data use at the federal and state level, local councils are not taking full advantage of their data for a variety of reasons. This paper reviews the status of open datasets of Australian local governments and reports progress being made in several student and other projects to develop open data web services using machine learning for smart cities.

scholarly journals Hands-on training about overfitting

2021 ◽  
Vol 17 (3) ◽  
pp. e1008671
Author(s):  
Janez Demšar ◽  
Blaž Zupan
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Computational Biology ◽  
Data Visualization ◽  
Data Science ◽  
Teaching Goals ◽  
Open Source Data ◽  
Hands On ◽  
Source Data ◽  
Critical Problems

Overfitting is one of the critical problems in developing models by machine learning. With machine learning becoming an essential technology in computational biology, we must include training about overfitting in all courses that introduce this technology to students and practitioners. We here propose a hands-on training for overfitting that is suitable for introductory level courses and can be carried out on its own or embedded within any data science course. We use workflow-based design of machine learning pipelines, experimentation-based teaching, and hands-on approach that focuses on concepts rather than underlying mathematics. We here detail the data analysis workflows we use in training and motivate them from the viewpoint of teaching goals. Our proposed approach relies on Orange, an open-source data science toolbox that combines data visualization and machine learning, and that is tailored for education in machine learning and explorative data analysis.

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
Learning Methods ◽  
Comprehensive Review
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