Universidade de Lisboa University of Lisbon. Instituto Superior de Ciências Sociais e Políticas School of Social and Political Sciences

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Universidade de Lisboa University of Lisbon. Instituto Superior de Ciências Sociais e Políticas School of Social and Political Sciences

Universidade de Lisboa University of Lisbon. Instituto Superior de Ciências Sociais e Políticas School of Social and Political Sciences

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Universidade de Lisboa University of Lisbon. Instituto Superior de Ciências Sociais e Políticas School of Social and Political Sciences

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Inválido para efeitos de certificação

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Universidade de Lisboa University of Lisbon. Instituto Superior de Ciências Sociais e Políticas School of Social and Political Sciences

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PROBABILIDADES E ESTATÍSTICA E PROCESSOS ESTOCÁSTICOS

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Inválido para efeitos de certificação

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Inválido para efeitos de certificação

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Inválido para efeitos de certificação

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Estabelecimento Unidade Orgânica Plano de Estudos Study Plan Tipo (diurno ou pós-laboral) Type Universidade de Lisboa University of Lisbon Instituto Superior de Ciências Sociais e Políticas School of Social and Political Sciences Licenciatura em Gestão de Recursos Humanos (pós-laboral) Licenciado in Human Resources Management Nocturno Nocturno Descritor Subject File Unidade Curricular Subject Docente responsável e respectivas horas de contacto Lecturer in charge and its contact time Outros docentes e respectivas horas de contacto Other lecturers and their contact time Estatística para a Gestão II Statistics for Management II Carlos Gonçalves cgoncalves@iscsp.ulisboa.pt 42 h Ano Lectivo Academic Year 2018-19 Ano Curricular Course Year 2 º 2 nd Semestre Semester 2º Créditos ECTS ECTS Credits Carga Lectiva Teaching Load Língua Language 3.231 horas/semana 3.231 hours/week Português Português Tempo Trabalho Workload Total: 130 h Contacto: TP = 42 h OT = 20 h Objectivos de aprendizagem (conhecimentos, aptidões e competências a desenvolver pelos estudantes) O1: Conhecer as metodologias e técnicas estatísticas univariadas e multivariadas aplicadas à gestão, no contexto da Gestão de Recursos Humanos. O2: Conhecer os indicadores estatísticos utilizados pelas empresas na área da Gestão de Recursos Humanos. O3: Saber operacionalizar a área da Estatística para a Gestão, relacionada com a Human Resources Analytics, visando a resolução de problemas estruturados e não-estruturados da Gestão de Recursos Humanos. Learning outcomes of the curricular unit O1: To know the univariate and multivariate statistical techniques applied to management, in the context of Human Resources Management. O2: To know the statistical indicators used by companies in the area of Human Resources Management. O3: To know how to operacionalize the area of Statistics for Management, related to Human Resources Analytics, aimed at the solution of structured and non-structured problems within Human Resources Management.

Conteúdos Programáticos - Síntese 1. Estatística aplicada à Gestão, no contexto da Gestão de Recursos Humanos: indicadores estatísticos, reporte de informação e análise estatística. 2. Human Resources Analytics aplicada à resolução de problemas de Gestão de Recursos Humanos. Resumed Syllabus 1. Statistics applied to Management, in the context of Human Resources Management: statistical indicators, information reporting and statistical analysis. 2. Human Resources Analytics applied to the solving of problems of Human Resources Management Conteúdos Programáticos 1. Estatística aplicada à Gestão, no contexto da Gestão de Recursos Humanos 1.1. Indicadores estatísticos de Gestão de Recursos Humanos: análise e reporte da informação estatística 1.2. Estatística aplicada à Gestão de Recursos Humanos no contexto da Human Resources Analytics (HRA) 1.2.1. Árvores de classificação 1.2.2. Redes neurais artificiais 1.2.3. Análise de componentes principais 1.2.4. Técnicas de regressão 2. Sistemas de Informação Estatística para a Gestão de Recursos Humanos 2.1. O papel da Estatística na Gestão de Sistemas de Informação 2.2. Advanced Business Languages no desenvolvimento de soluções Estatísticas para suporte à tomada de decisão em Gestão de Recursos Humanos 2.3. Sistemas Periciais no desenvolvimento de Ferramentas Estatísticas para: 2.3.1. Desenvolvimento de Estratégia de Recursos Humanos 2.3.2. Recrutamento e Selecção 2.3.3. Avaliação de Performance 2.3.4. Sistemas de recompensas 2.3.5. Higiene, Segurança e Saúde no Trabalho 2.3.6. Formação dos Recursos Humanos

Syllabus 1. Statistics applied to Management, in the context of Human Resources Management 1.1. Statistical indicators of Human Resources Management: statistical information analysis and report 1.2. Statistics applied to Human Resources Management in the context of Human Resources Analytics (HRA) 1.2.1. Classification trees 1.2.2. Artificial neural networks 1.2.3. Principal components analysis 1.2.4. Regression techniques 2. Systems of Statistical Information for Human Resources Management 2.1. Role of Statistics in Management Information Systems 2.2. Advanced Business Languages and development of Statistical solutions for support to decision making in Human Resources Management 2.3. Expert Systems in the development of Statistical Tools for: 2.3.1. Human Resources strategy development 2.3.2. Recruitment and selection 2.3.3. Performance evaluation 2.3.4. Reward systems 2.3.5. Hygiene, Safety and Health at Work 2.3.6. Human Resources Training Metodologia de Ensino e Avaliação A metodologia de ensino seguida é de ensino-investigação, com as seguintes componentes: - Expositiva, para a apresentação de conceitos chave; - Participativa, com a resolução de problemas, e aplicações da estatística e da análise de dados a casos exemplares reais de Gestão de Recursos Humanos; - Argumentativa, em conjugação com análise crítica de cenários e de estratégias em problemas decisionais concretos e com diferentes graus de complexidade; - Roleplaying, com exemplos de aplicação a problemas de Gestão de Recursos Humanos que exigem aplicação da Human Resources Analytics. Sistema de Avaliação: Avaliação Contínua com os seguintes elementos: dois testes valendo, cada um, 50% da nota final, sendo o primeiro teste em torno do primeiro bloco programático até ao ponto 1.2.2. (inclusive) e o segundo teste em torno dos pontos 1.2.3., 1.2.4. e segundo bloco programático.

Teaching and Assesment Methodologies The teaching methodology followed is of teaching-research, with the following components: - Expositive, for the presentation of key concepts; - Participative, with problem solving and applications of statistics and data analysis to real exemplary cases of Human Resources Management; - Argumentative, in conjugation with the critical analysis of scenarios and strategies, in concrete decisional problems and with varying degrees of complexity; - Roleplaying, with application examples to problems of Human Resources Management that demand the application of Human Resources Analytics. Assessment System: Intra-semester assessment (Continuous Assessment) with the following elements: two tests each with a 50% weight for the final grade, the first test covers the first syllabus block up to the syllabus' point 1.2.2. (inclusive), the second test covers the syllabus' points 1.2.3., 1.2.4. and the second syllabus block. Bibliografia principal - CHAPMANN, J., (2017), Machine Learning: Fundamental Algorithms for Supervised and Unsupervised Learning With Real-World Applications, CreateSpace. - EDWARDS, R. and EDWARDS, K. (2016), Predictive HR Analytics: Mastering the HR Metric, Kogan Page. - FITZ-ENZ, J., (2010), The New HR Analytics: Predicting the Economic Value of Your Company's Human Capital Investments, AMACOM. - DAVENPORT, T.H., HARRIS, J.G. and MORISON, R. (2010), Analytics At Work: Smarter Decisions, Better Results. Harvard Business Review Press. - PHILIPS, J. and PHILIPS, P.P. (2014). Making Human Capital Analytics Work: Measuring the ROI of Human Capital Processes and Outcomes. McGraw-Hill. - PEASE, G. and BERESFORD, B. (2014). Developing Human Capital: Using Analytics to plan and Optimize Your Learning and Development Investments. Wiley. - DOWNEY, A.B. (2012). Think Python. O'Reilly Media. - GONÇALVES, C.P. (2015). Python Aplicado a Problemas de Gestão de Recursos Humanos, Texto de Apoio - ISCSP.

Main Bibliography - CHAPMANN, J., (2017), Machine Learning: Fundamental Algorithms for Supervised and Unsupervised Learning With Real-World Applications, CreateSpace. - EDWARDS, R. and EDWARDS, K. (2016), Predictive HR Analytics: Mastering the HR Metric, Kogan Page. - FITZ-ENZ, J., (2010), The New HR Analytics: Predicting the Economic Value of Your Company's Human Capital Investments, AMACOM. - DAVENPORT, T.H., HARRIS, J.G. and MORISON, R. (2010), Analytics At Work: Smarter Decisions, Better Results. Harvard Business Review Press. - PHILIPS, J. and PHILIPS, P.P. (2014). Making Human Capital Analytics Work: Measuring the ROI of Human Capital Processes and Outcomes. McGraw-Hill. - PEASE, G. and BERESFORD, B. (2014). Developing Human Capital: Using Analytics to plan and Optimize Your Learning and Development Investments. Wiley. - DOWNEY, A.B. (2012). Think Python. O'Reilly Media. - GONÇALVES, C.P. (2015). Python Aplicado a Problemas de Gestão de Recursos Humanos, Texto de Apoio - ISCSP. Data Date 26-07-2018