An Essay on How Data Science Can Strengthen Business

• Autor(a) principal: Santos, António Duarte
• Data de Publicação: 2023
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/11144/6334
• topic: data science; applied data; technology; organizations.

Data science combines several extensions, including, e.g., statistics, scientific methods, artificial intelligence (AI) and data analysis to extract value from raw data. Analytical applications and data scientists can then verify and defer the results to discover patterns and trends. In this way, they allow business leaders to gain enlightened knowledge about the market. Companies have kept a wealth of data with them. As modern technology allowed for the creation and storage of ever-increasing amounts of information, data volumes popped. The wealth of data collected and stored by these technologies can bring regenerative benefits to organizations and societies around the world, but only if they can interpret it. That's where data science comes in. So, the applied economics refers to the application of economic theory and analysis. In this article we intend to present several software that are available for the application of economic analysis. Analysis can be performed on any type of data and is a way of looking at raw data and find useful information. There are several technologies available for economic analysis, with more or less characteristics, some of which are not only intended for this single purpose, and cover a wider spectrum of functionalities. Some of the technologies we will use are, e.g., Rstudio, SPSS, Statis and SAS/Stata. These are very common technologies when talking about economic or business analysis. The intention is to demonstrate how each of these software analyse the data and subsequently the interpretations that we can draw from that scrutiny. Organizations are using data science teams to turn data into a competitive advantage by refining products and services and cost-effective solutions. We will use some different algorithms to verify how they are processed by the different technologies, namely we will use metrics such as maximum, minimum, covariance, standard deviation, average and multicollinearity and variance, even the use of types of regression models.