使用SQL和PowerBi分析Covid-19的日常结果和患者信息

IF 3.4 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Data Science and Analytics Pub Date : 2023-05-20 DOI:10.18517/ijods.4.1.1-9.2023

Sulejman Karamehic

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引用次数: 0

摘要

COVID-19是一种由严重急性呼吸综合征冠状病毒2 (SARS-CoV-2)病毒引起的传染病。首例已知病例于2019年12月在中国武汉被发现。该疾病迅速在全球传播，导致COVID-19大流行。COVID-19大流行的爆发对整个世界产生了巨大影响，对数据的分析在很多方面都有意义，可以更好地了解它对我们社会的影响。本文旨在分析基于国家和大陆层面的COVID-19每日信息，了解病例数和死亡人数及其关系，此外，这是为了更好地了解按国家分列的疫苗接种数量和病例/死亡的影响，它们是如何影响这些数字的。该解决方案是在对包含每个国家日常信息的数据集进行分析的基础上创建的，并使用MySQL, SQL和PowerBi以查询结果的方式生成这项工作的结果，这些结果已使用PowerBi转换为视觉效果，以便更好地理解该主题的进一步研究工作。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Analysis of Covid-19 daily results and information about patients using SQL and PowerBi

COVID-19 is a contagious disease caused by a virus, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first known case was identified in Wuhan, China, in December 2019. The disease quickly spread worldwide, resulting in the COVID-19 pandemic. The outburst of COVID-19 pandemic had tremendous effect on the whole world and analysis of the data can be meaningful in many ways to better understand the effect it had in our society. This paper aims in the direction of analysis of COVID-19 daily information based on country and continent level in terms of understanding the number of cases and deaths and their relationship, besides this is aims to better understand the vaccination number by country and effect of cases/death how they have affected these numbers. The solution was created on analysis of a dataset that contains daily information on each country, and using MySQL, SQL and PowerBi to generate the results for this work in way of query results which have been transformed to visuals using PowerBi for better understanding for further research work on this topic.

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来源期刊

International Journal of Data Science and Analytics Multiple-

CiteScore

6.40

自引率

8.30%

发文量

期刊介绍： Data Science has been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. The field encompasses the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. It also tackles related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation.The International Journal of Data Science and Analytics (JDSA) brings together thought leaders, researchers, industry practitioners, and potential users of data science and analytics, to develop the field, discuss new trends and opportunities, exchange ideas and practices, and promote transdisciplinary and cross-domain collaborations. The journal is composed of three streams: Regular, to communicate original and reproducible theoretical and experimental findings on data science and analytics; Applications, to report the significant data science applications to real-life situations; and Trends, to report expert opinion and comprehensive surveys and reviews of relevant areas and topics in data science and analytics.Topics of relevance include all aspects of the trends, scientific foundations, techniques, and applications of data science and analytics, with a primary focus on:statistical and mathematical foundations for data science and analytics;understanding and analytics of complex data, human, domain, network, organizational, social, behavior, and system characteristics, complexities and intelligences;creation and extraction, processing, representation and modelling, learning and discovery, fusion and integration, presentation and visualization of complex data, behavior, knowledge and intelligence;data analytics, pattern recognition, knowledge discovery, machine learning, deep analytics and deep learning, and intelligent processing of various data (including transaction, text, image, video, graph and network), behaviors and systems;active, real-time, personalized, actionable and automated analytics, learning, computation, optimization, presentation and recommendation; big data architecture, infrastructure, computing, matching, indexing, query processing, mapping, search, retrieval, interoperability, exchange, and recommendation;in-memory, distributed, parallel, scalable and high-performance computing, analytics and optimization for big data;review, surveys, trends, prospects and opportunities of data science research, innovation and applications;data science applications, intelligent devices and services in scientific, business, governmental, cultural, behavioral, social and economic, health and medical, human, natural and artificial (including online/Web, cloud, IoT, mobile and social media) domains; andethics, quality, privacy, safety and security, trust, and risk of data science and analytics