{"id":1311,"date":"2022-02-08T00:00:00","date_gmt":"2022-02-07T23:00:00","guid":{"rendered":"https:\/\/atlax360.com\/?p=1311"},"modified":"2023-03-27T09:53:56","modified_gmt":"2023-03-27T07:53:56","slug":"tendencias-data-science-mas-faciles-accesibles","status":"publish","type":"post","link":"https:\/\/atlax360.com\/pt-pt\/blog\/tendencias-data-science-mas-faciles-accesibles\/","title":{"rendered":"Tend\u00eancias Data Science para 2022: mais f\u00e1cil e acess\u00edvel"},"content":{"rendered":"<p><em>Data science<\/em> ou a ci\u00eancia dos dados \u00e9 um campo interdisciplinar que alavanca a matem\u00e1tica, a programa\u00e7\u00e3o,<br \/>\nos conhecimentos empresariais e de dom\u00ednio para resolver problemas de dados complexos. A sua emerg\u00eancia como campo<br \/>\nde estudo e aplica\u00e7\u00e3o<br \/>\npr\u00e1tica nos \u00faltimos anos tem permitido o desenvolvimento de diferentes tecnologias, especialmente a aprendizagem de<br \/>\nm\u00e1quinas (<em>Machine Learning<\/em>) como forma de trabalhar para aquilo a que chamamos <em>Intelig\u00eancia<br \/>\nArtificial<\/em> (IA), um campo<br \/>\nde tecnologia que est\u00e1 a transformar rapidamente a forma como trabalhamos e vivemos.<\/p>\n<p>A vasta quantidade de dados recolhidos e armazenados por estas tecnologias pode trazer benef\u00edcios transformadores<br \/>\npara organiza\u00e7\u00f5es e sociedades a n\u00edvel mundial, mas apenas se soubermos como interpret\u00e1-los. Neste artigo iremos<br \/>\nrever as tend\u00eancias<br \/>\nna <em>ci\u00eancias dos dados <\/em> para o pr\u00f3ximo ano, que promete continuar a acelera\u00e7\u00e3o digital vivida nos \u00faltimos<br \/>\ndois anos devido \u00e0 COVID-19.<\/p>\n<h3>Quatro tend\u00eancias Data Science para 2022<\/h3>\n<p>A import\u00e2ncia crescente da <em> ci\u00eancias dos dados <\/em> tamb\u00e9m no mundo dos neg\u00f3cios e do com\u00e9rcio torna a ci\u00eancia<br \/>\npor detr\u00e1s deste campo cada vez mais acess\u00edvel. Isto levou a uma democratiza\u00e7\u00e3o generalizada da ci\u00eancia dos dados,<br \/>\nque sem d\u00favida veremos<br \/>\nentre as tend\u00eancias de aprendizagem nos pr\u00f3ximos anos. Mas dentro do campo em quest\u00e3o, encontram <strong>quatro<br \/>\ntend\u00eancias chave:<\/strong><\/p>\n<h3>1. Small Data ou TinyML<\/h3>\n<p>Enquanto a atividade de desenvolvimento da aprendizagem de m\u00e1quinas se concentra em solu\u00e7\u00f5es de alta pot\u00eancia na<br \/>\nnuvem ou em solu\u00e7\u00f5es de m\u00e9dia pot\u00eancia no limite, h\u00e1 outro conjunto de actividades destinadas a implementar a<br \/>\naprendizagem de m\u00e1quinas<br \/>\nem sistemas com recursos muito limitados: TinyML. Esta ideia adquiriu um relevo significativo nos \u00faltimos anos,<br \/>\ntenso sido impulsionada pela no\u00e7\u00e3o de que alguns problemas n\u00e3o precisam de um servidor de data center ou de uma<br \/>\nplataforma de ponta<br \/>\npara resolv\u00ea-los. Em suma, \u00e9 uma metodologia que procura aprimorar solu\u00e7\u00f5es e ampliar os limites para os quais<br \/>\nrealmente \u00e9 necess\u00e1ria uma computa\u00e7\u00e3o mais potente.<\/p>\n<p>Durante 2022, v\u00ea-lo-emos aparecer cada vez mais em sistemas incorporados: desde alertas de desgaste de<br \/>\neletrodom\u00e9sticos, autom\u00f3veis ou equipamentos industriais, tornando-os todos mais inteligentes e \u00fateis.<\/p>\n<h3>2. AutoML ou Auto Machine Learning<\/h3>\n<p>Muitas vezes, uma grande parte do tempo de um cientista de dados \u00e9 gasto na limpeza e prepara\u00e7\u00e3o de dados, que s\u00e3o<br \/>\ntarefas de conhecimento intensivo e muitas vezes repetitivas. AutoML <em>(auto machine learning)<\/em> \u00e9 a<br \/>\nautomatiza\u00e7\u00e3o destas tarefas,<br \/>\ne o seu objectivo a curto prazo \u00e9 que qualquer pessoa com um problema a resolver ou uma ideia a testar possa aplicar<br \/>\na autoaprendizagem da m\u00e1quina para poupar tempo e dedic\u00e1-la ao desenvolvimento de solu\u00e7\u00f5es. Esta tend\u00eancia est\u00e1 a<br \/>\nimpulsionar<br \/>\na democratiza\u00e7\u00e3o da ci\u00eancia dos dados que mencion\u00e1mos no in\u00edcio do artigo e que veremos nos pr\u00f3ximos anos.<\/p>\n<h3>3. Experi\u00eancia do cliente baseada em dados<\/h3>\n<p>Tal como discutimos, a ci\u00eancia dos dados est\u00e1 a tornar-se cada vez mais importante nos neg\u00f3cios e no com\u00e9rcio. As<br \/>\nintera\u00e7\u00f5es entre os utilizadores e as empresas est\u00e3o a tornar-se cada vez mais digitais, o que se traduz na<br \/>\ncapacidade de medir e analisar<br \/>\no comportamento dos utilizadores. Como resultado, at\u00e9 2022 veremos mais empresas a utilizar dados dos utilizadores<br \/>\npara proporcionar experi\u00eancias mais personalizadas, valiosas e agrad\u00e1veis para os clientes, e experi\u00eancias mais<br \/>\nf\u00e1ceis e acess\u00edveis<br \/>\npara as empresas.<\/p>\n<h3>4. Formas h\u00edbridas de automatiza\u00e7\u00e3o<\/h3>\n<p>A automatiza\u00e7\u00e3o \u00e9 uma constante nas publica\u00e7\u00f5es de novas tend\u00eancias, com tecnologias como a automatiza\u00e7\u00e3o de<br \/>\nprocessos rob\u00f3ticos (RPA) a avan\u00e7arem rapidamente para fornecer resultados de alto valor atrav\u00e9s da sua an\u00e1lise<br \/>\nbem-sucedida de dados modernos,<br \/>\ncom grande volumetria e altamente complexos. Mas esta automatiza\u00e7\u00e3o pode ser otimizada ao procurar modelos h\u00edbridos<br \/>\ncom envolvimento humano, j\u00e1 que oferece \u00e0s empresas a capacidade de processar dados estruturados e n\u00e3o estruturados<br \/>\ne utilizar<br \/>\na abstra\u00e7\u00e3o humana em pontos cr\u00edticos de decis\u00e3o. Ao identificar os principais candidatos \u00e0 RPA h\u00edbrida e ao<br \/>\nutilizar uma abordagem de melhores pr\u00e1ticas na implementa\u00e7\u00e3o, as empresas podem multiplicar a efic\u00e1cia das suas<br \/>\niniciativas de RPA e permanecer<br \/>\ncompetitivas \u00e0 medida que a tecnologia de automa\u00e7\u00e3o evolui.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>El data science o ciencia de datos es un campo interdisciplinario que aprovecha las matem\u00e1ticas, la programaci\u00f3n, los negocios y el conocimiento del dominio&#8230;<\/p>\n","protected":false},"author":4,"featured_media":933,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[42,41],"tags":[],"class_list":["post-1311","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-todos-posts","category-transformacao-digital"],"yoast_head":"<!-- This site is optimized with the Yoast SEO 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