![]() Os resultados apontam carência de trabalhos que utilizam: técnicas de predição, visualizações sofisticadas, e análise dos modelos de visualização para a natureza das variáveis. Para isso, essa pesquisa aborda uma revisão sistemática da literatura composta por quatro etapas: revisão terciária, planejamento, condução e interpretação dos resultados. Logo, este artigo tem o objetivo de responder a seguinte questão: "Quais são as lacunas da literatura acerca dos trabalhos que apresentam modelos de visualizações no contexto educacional?". A visualização de informações referentes aos dados de estudantes auxilia no processo de ensino-aprendizado. The results point to the need for works that use: forecasting techniques, detailed visualizations, and analysis of visualization models for the nature of the variables. ![]() For this, this research addresses a systematic review of the literature composed of four stages: tertiary review, planning, conduction and interpretation of results. Therefore, this article aims to answer the following question: "What are the gaps in the literature about works that present visualization models in the educational context?". You will exam the importance of using the "right" amount of color and in the right place and be able to apply design principles to de-clutter your data visualization.The visualization of information regarding the data of auxiliary students in the teaching-learning process. You will evaluate pre-attentive attributes and why they are important in visualizations. You will apply effective best practice design principles to your data visualizations and be able to illustrate examples of strategic use of contrast to highlight important elements. You will assess how data and design work together, including how to choose the appropriate visual representation for your data, and the difference between effective and ineffective visuals. You will define and examine the similarities and differences of exploratory and explanatory analysis as well as begin to ask the right questions about what’s needed in a visualization. This course assumes you understand the tools within Tableau and have some knowledge of the fundamental concepts of data visualization. In this course, you will analyze and apply essential design principles to your Tableau visualizations. The Specialization culminates in a Capstone Project in which you will use sample data to create visualizations, dashboards, and data models to prepare a presentation to the executive leadership of a fictional company. You will apply predicative analytics to improve business decision making. You will use Tableau to create high-impact visualizations of common data analyses to help you see and understand your data. You will view examples from real world business cases and journalistic examples from leading media companies.īy the end of this specialization, you will be able to generate powerful reports and dashboards that will help people make decisions and take action based on their business data. We leverage Tableau's library of resources to demonstrate best practices for data visualization and data storytelling. This Specialization, in collaboration with Tableau, is intended for newcomers to data visualization with no prior experience using Tableau. Being able to use this data provides huge opportunities and to turn these opportunities into reality, people need to use data to solve problems. And 75 times the number of information sources (IDC, 2011). In 2020 the world will generate 50 times the amount of data as in 2011.
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