{"id":75,"date":"2025-11-02T21:46:59","date_gmt":"2025-11-03T00:46:59","guid":{"rendered":"https:\/\/web.inf.ufpr.br\/mba-dsbd\/?page_id=75"},"modified":"2025-11-02T22:28:29","modified_gmt":"2025-11-03T01:28:29","slug":"disciplinas","status":"publish","type":"page","link":"https:\/\/web.inf.ufpr.br\/mba-dsbd\/disciplinas\/","title":{"rendered":"Disciplinas"},"content":{"rendered":"\t\t
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\nM\u00f3dulos\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Vis\u00e3o geral<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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A imagem abaixo fornece uma vis\u00e3o geral e integrada do conte\u00fado visto no Curso de Especializa\u00e7\u00e3o em Data Science & Big Data. As 6 disciplinas de 60 horas est\u00e3o exibidas com as ramifica\u00e7\u00f5es em m\u00f3dulos e t\u00f3picos. Somando-se a estas, tem-se a disciplina de Metodologia de Pesquisa. As 7 disciplinas s\u00e3o detalhadas nas pr\u00f3ximas sess\u00f5es.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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M\u00f3dulo I - Fundamentos para Data Science e Big Data<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Infraestrutura Computational (60 hrs)<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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  1. Infraestrutura de software.
    • Shell script para automatizar tarefas.<\/li>
    • Estrutura de sistemas operacionais.<\/li>
    • Fundamentos de Engenharia de Software para Data Science.<\/li><\/ul><\/li>
    • Infraestrutura de comunica\u00e7\u00e3o.
      • Conceitos de aplica\u00e7\u00e3o de redes.<\/li>
      • Protocolos web.<\/li>
      • Nuvem computacional.<\/li><\/ul><\/li>
      • Infraestrutura de alto desempenho.
        • Paralelismo e distribui\u00e7\u00e3o.<\/li>
        • Clusters computacionais.<\/li>
        • Submiss\u00e3o e controle de tarefas em clusters.<\/li><\/ul><\/li><\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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          Infer\u00eancia estat\u00edstica para ci\u00eancia de dados (60 hrs)<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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          1. Introdu\u00e7\u00e3o ao C\u00e1lculo diferencial Integral.
            • Fun\u00e7\u00f5es, limites e continuidade.<\/li>
            • Derivadas e Integrais.<\/li><\/ul><\/li>
            • \u00c1lgebra linear aplicada.
              • Vetores e Matrizes.<\/li>
              • Solu\u00e7\u00e3o de sistemas lineares.<\/li>
              • Solu\u00e7\u00e3o de sistemas n\u00e3o-lineares.<\/li><\/ul><\/li>
              • Otimiza\u00e7\u00e3o.
                • Programa\u00e7\u00e3o linear.<\/li>
                • Programa\u00e7\u00e3o quadr\u00e1tica.<\/li>
                • Programa\u00e7\u00e3o n\u00e3o-linear.<\/li><\/ul><\/li>
                • Introdu\u00e7\u00e3o \u00e0 probabilidade e vari\u00e1veis aleat\u00f3rias.
                  • Distribui\u00e7\u00f5es de probabilidade.<\/li>
                  • Esperan\u00e7a, vari\u00e2ncia e covari\u00e2ncia.<\/li>
                  • Resultados assint\u00f3ticos e suas aplica\u00e7\u00f5es.<\/li><\/ul><\/li>
                  • Paradigmas e elementos de infer\u00eancia estat\u00edstica.
                    • Estima\u00e7\u00e3o e fun\u00e7\u00f5es de evid\u00eancia.<\/li>
                    • Intervalos de confian\u00e7a e testes de hip\u00f3teses.<\/li>
                    • Infer\u00eancia baseada em simula\u00e7\u00e3o.<\/li><\/ul><\/li><\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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                      Linguagens de programa\u00e7\u00e3o para ci\u00eancia de dados (60 hrs)<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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                      1. Introdu\u00e7\u00e3o e configura\u00e7\u00e3o do ambiente de programa\u00e7\u00e3o (R e python).
                        • Opera\u00e7\u00f5es aritm\u00e9ticas e l\u00f3gicas. Estruturas de controle e repeti\u00e7\u00f5es.<\/li>
                        • Programa\u00e7\u00e3o orientada \u00e0 objetos e fun\u00e7\u00f5es.<\/li>
                        • Introdu\u00e7\u00e3o ao pair programing.<\/li><\/ul><\/li>
                        • Leitura e manipula\u00e7\u00e3o de dados.
                          • Acessando base de dados.<\/li>
                          • Aquisi\u00e7\u00e3o de dados pela internet (web scrapping<\/em>).<\/li><\/ul><\/li>
                          • An\u00e1lise explorat\u00f3ria e visualiza\u00e7\u00e3o interativa de dados.<\/li><\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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                            M\u00f3dulo II - M\u00e9todos contempor\u00e2neos em Data Science e Big Data<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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                            Processamento de Big Data<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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                            1. Introdu\u00e7\u00e3o ao Big Data.
                              • Organiza\u00e7\u00e3o f\u00edsica e l\u00f3gica.<\/li>
                              • M\u00e9todos de acesso.<\/li><\/ul><\/li>
                              • Armanezamento de Big Data
                                • SQL, NoSQL e NewSQL.<\/li>
                                • Data warehouse, data cleaning e Integra\u00e7\u00e3o.<\/li>
                                • Plataformas distribu\u00eddas (programa\u00e7\u00e3o MapReduce, Hadoop e Spark).<\/li><\/ul><\/li><\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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                                  Modelos estat\u00edsticos<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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                                  1. Modelos lineares
                                    • Modelos lineares generalizados.<\/li>
                                    • Modelos aditivos generalizados.<\/li>
                                    • Sele\u00e7\u00e3o de modelos e penaliza\u00e7\u00e3o.<\/li><\/ul><\/li>
                                    • \u00c1rvores de classifica\u00e7\u00e3o e regress\u00e3o.<\/li>
                                    • Regress\u00e3o multivariada.<\/li>
                                    • Introdu\u00e7\u00e3o \u00e0 Infer\u00eancia Causal.<\/li>
                                    • S\u00e9ries temporais.<\/li><\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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                                      Minera\u00e7\u00e3o de dados e aprendizagem de m\u00e1quina<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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                                      1. Introdu\u00e7\u00e3o ao machine learning<\/em>.
                                        • Representa\u00e7\u00e3o de dados e engenharia de caracter\u00edsticas.<\/li>
                                        • Aprendizagem supervisionada.<\/li>
                                        • Aprendizagem n\u00e3o-supervisionada.<\/li>
                                        • Redu\u00e7\u00e3o de dados\/dimens\u00e3o.<\/li>
                                        • Avalia\u00e7\u00e3o e melhoria de modelos.<\/li><\/ul><\/li>
                                        • Introdu\u00e7\u00e3o ao Grandes Modelos de Linguagem (Large Language Models).<\/li><\/ol>

                                          Permeando aos m\u00f3dulos I e II ser\u00e1 apresentado a disciplina de M\u00e9todos de Pesquisa que visa preparar o aluno para o desenvolvimento do projeto final, bem como, para programas avan\u00e7ados como mestrado e doutorado nas \u00e1reas de estat\u00edstica e ci\u00eancia da computa\u00e7\u00e3o.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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                                          M\u00e9todos de pesquisa<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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                                          1. Tipos de estudos e o m\u00e9todo cient\u00edfico.<\/li>
                                          2. Estrutura do artigo cient\u00edfico.<\/li>
                                          3. M\u00e9todos e p\u0155atica da pesquisa reproduz\u00edvel.<\/li>
                                          4. Introdu\u00e7\u00e3o \u00e0s t\u00e9cnicas de apresenta\u00e7\u00e3o e reda\u00e7\u00e3o cient\u00edfica.<\/li>
                                          5. T\u00e9cnicas e ferramentas para elabora\u00e7\u00e3o de relat\u00f3rios din\u00e2micos.<\/li><\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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                                            Formato do curso<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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                                            O curso de especializa\u00e7\u00e3o em Data Science e Big Data ocorrer\u00e1 \u00e0s sextas-feiras (19:00\u201323:00) e aos s\u00e1bados (08:00\u201312:00). Ser\u00e3o dois m\u00f3dulos de 4 horas por final de semana, tendo 15 semanas de dura\u00e7\u00e3o por semestre.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

                                            M\u00f3dulos Vis\u00e3o geral A imagem abaixo fornece uma vis\u00e3o geral e integrada do conte\u00fado visto no Curso de Especializa\u00e7\u00e3o em […]<\/p>\n","protected":false},"author":109,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"elementor_header_footer","meta":{"site-sidebar-layout":"no-sidebar","site-content-layout":"","ast-site-content-layout":"full-width-container","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"class_list":["post-75","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/web.inf.ufpr.br\/mba-dsbd\/wp-json\/wp\/v2\/pages\/75","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/web.inf.ufpr.br\/mba-dsbd\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/web.inf.ufpr.br\/mba-dsbd\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/web.inf.ufpr.br\/mba-dsbd\/wp-json\/wp\/v2\/users\/109"}],"replies":[{"embeddable":true,"href":"https:\/\/web.inf.ufpr.br\/mba-dsbd\/wp-json\/wp\/v2\/comments?post=75"}],"version-history":[{"count":4,"href":"https:\/\/web.inf.ufpr.br\/mba-dsbd\/wp-json\/wp\/v2\/pages\/75\/revisions"}],"predecessor-version":[{"id":80,"href":"https:\/\/web.inf.ufpr.br\/mba-dsbd\/wp-json\/wp\/v2\/pages\/75\/revisions\/80"}],"wp:attachment":[{"href":"https:\/\/web.inf.ufpr.br\/mba-dsbd\/wp-json\/wp\/v2\/media?parent=75"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}