filmeu
Imagem formacao defeito 1
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Formação Livre Data Science com Python

Lisboa
O curso Data Science com Python visa introduzir os princípios introdutórios de Data Science com recurso à linguagem de programação Python. Este curso irá iniciar-se e desenvolver-se no domínio da programação orientada a objetos, com foco no desenvolvime... Ler mais

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Horas32h Créditos4 ECTS
  • Apresentação

    Apresentação

    O curso Data Science com Python visa introduzir os princípios introdutórios de Data Science com recurso à linguagem de programação Python. Este curso irá iniciar-se e desenvolver-se no domínio da programação orientada a objetos, com foco no desenvolvimento de competências ao nível da análise e interpretação de dados. Construído sobre uma metodologia prática e de proximidade a contextos reais, os formandos irão compreender e resolver os problemas associados a domínios interdisciplinares como, por exemplo, Ciências e Atendimento Médico, Farmacologia, Banca e Redes Sociais. A complementaridade entre o estado da arte e as boas práticas profissionais, alinharão os formandos com as principais tendências de contratação e evolução de carreira.
  • Competências

    Competências

    (i) processar dados em bruto a partir de bases de dados disponibilizadas ou construídas em ambiente empresarial ou de investigação académica; (ii) construir e implementar modelos que possibilitem testes de hipóteses e/ou extração de conclusões por via de inferência estatística; (iii) compreender, selecionar e construir métodos de visualização de dados; (iv) compreender os princípios introdutórios de Machine Learning em termos dos problemas de classificação e regressão.
  • Objetivos

    Objetivos

    São objetivos do presente curso: - Desenvolver capacidades de programação; - Desenvolver capacidades de análise e interpretação de conjuntos de dados; - Desenvolver capacidades de criação e implementação de modelos com base em dados; - Desenvolver capacidades ao nível da comunicação visual sobre conjunto de dados.
  • Destinatários

    Destinatários

    Alunos do Ensino Superior ou Profissionais das áreas das Ciências ou Engenharias sem experiência em programação, ou experiência elementar em programação, que visem adquirir conhecimento na área das Ciências dos Dados.
  • Programa

    Programa

    • [Anual]
    • Data Science com Python 4 ects
  • Saídas Profissionais

    Saídas Profissionais

    Os relatórios - U.S. Bureau of Labor Statistics - reportam que a necessidade de profissionais em Data Science irá crescer a uma média de 27.9%/ano até 2026. São referenciadas como principais saídas profissionais: - Data Scientists: profissionais que encontram, processam e instrumentalização dados de empresas, para suporte estatístico à tomada de decisões. - Machine Learning Engineers/ Scientists: profissionais que padronizam metodologias de processamento e análise de dados, convergindo para o desenvolvimento de soluções finais.
  • Observações

    Observações

    Data de Início Prevista: 18/12/2023. Data limite para as candidaturas: 19/12/2023. Formato: online Datas (8 Sessões - 9h - 13h) Dias: 18, 19, 20, 21, 22, 27, 28 e 29 de Dezembro 2023 Programa do Curso 1 - Introdução a Data Science (2 h) 2 - Introdução a Python (12 h) 2.1 Enquadramento Tecnológico e Aplicabilidade Profissional 2.2 Formatação de Espaços em Branco 2.3 Módulos 2.4 Aritmética 2.5 Funções 2.6 Strings 2.7 Lists 2.8 Tuples 2.9 Dictionaries 2.10 Sets 2.11 Controlo e Fluxo 2.12 Ordenação, Aleatoriedade, Geração e Iteração 2.13 Expressões Regualres 2.14 Programação Orientada a Objectos 2.15 Args e Kwargs 3 - Raw Data: Limpar e Operar Dados em Bruto (4 h) 3.1 Arrays, operadores matemáticos, indexação e manipulação 3.2 Estruturas de Dados 3.3 Inserção e Exportação de Dados 3.4 Limpeza de Dados: Verificação 4 - Inferência Estatística: Tópicos em Data Science (4 h) 4.1 Distribuições I 4.2 Z-score 4.3 p-value 4.4 Testes unilaterais e bilaterais 4.5 Erros tipo 1 e tipo 2 4.6 Intervalos de confiança 4.7 Correlação 4.8 Teste Z vs. Teste T 4.9 Distribuições II 4.10 Teste Qui Quadrado para Independência 5 - Data Visualization: Gráficos, Seleção e Abordagens (5 h) 5.1 Gráficos de Linhas 5.2 Criação de Múltiplos Gráficos 5.3 Estilização e Grafismo 5.5 Box plots 5.5 Heatmaps 5.6 Scatter plots, histogramas e matrizes 5.7 Area plots 5.8 Bubble plots 5.9 Hexagon binning 5.10 Trellis plots 5.11 3D plots 6 - Machine Learning: Princípios Básicos (5 h) 6.1 Diferentes tipos de Machine Learning 6.2 Árvores de Decisão 6.3 Regressão Linear 6.4 Regressão Logística 6.5 Classificação Bayesiana 6.6 k-means clustering 6.7 Hierarchical clustering

Centro Universitário

Lisboa | Faculdade de Engenharia
INSCREVER-ME

Direção do Curso

Prof. Dr. Tiago Cunha Reis

Secretariado

Tânia Pereira

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