Luiz Paulo Lopes Fávero.

Full Professor at the Faculty of Economics, Administration and Accounting at the University of São Paulo (FEA/USP) and advisor in the Master's and Doctoral programs at FEA/USP and at the Polytechnic School of USP (POLI/USP). He has a Post-Doctorate in Financial Econometrics from Columbia University in New York. He is a Full Professor at FEA/USP (emphasis on Quantitative Methods and Econometric Modeling). He holds a Master's and Doctor's degree in Business Administration (emphasis in Organizational Economics) from FEA/USP, having received an honorable mention for both works. He graduated in Engineering from Escola Politécnica da USP and has a postgraduate degree in Business Administration (CEAG) from FGV/EAESP. He participated in Econometric Modeling courses at California State University and Universidad de Salamanca, and in Case Studies at Harvard Business School. He is a member of the Board of Directors of the Global Business Research Committee and a founding member of the Iberoamerican Academy of Data Science. He was founder and editor-in-chief of the International Journal of Multivariate Data Analysis. He is a columnist for IT Mídia IDGNow. He works in the area of ​​Data Science, Business Analytics and Business Intelligence, with an emphasis on data analysis, multivariate modeling, machine learning, operational research, microeconometrics and mathematical and statistical models applied to corporate risk management, organizational performance and company valuation. He is the author of the books Data Science for Business and Decision Making (English and Korean editions, 2019), Data Analysis Handbook: Statistics and Multivariate Modeling with Excel®, SPSS® and Stata® (2017), Data Analysis: Regression with Excel®, Stata® and SPSS® (2015), Data Analysis: Exploratory Multivariate Techniques with SPSS® and Stata® (2015), Quantitative Methods with Stata® (2014), Operations Research for Engineering Courses (2013), Operations Research for Business, Accounting and Economics Courses (2012) and Data Analysis: Multivariate Modeling for Decision Making (2009), and co-author of Contemporary Studies in Economics and Financial Analysis (2011) and Trends in International Trade Issues (2006) . He is a globally accredited teacher by StataCorp and Timberlake. Estimate and implement models using Machine Learning, Big Data and Analytics tools for Decision Making, such as Python, R, Stata, SAS, IBM SPSS and Minitab. He is the author of the OVERDISP Package for Stata, a computational module registered by StataCorp and the SSC Archive (Statistical Software Components) of the Boston College Department of Economics. He is one of the authors of the OVERDISP library for R, a computational module registered by CRAN (The Comprehensive R Archive Network). He is one of the authors of the OVERDISP, STEPWISE, SHAPIRO_FRANCIA and VUONG_TEST libraries for Python, computational modules registered by PyPI (Python Package Index Repository of Software for the Python Programming Language). He is a finalist for the Jabuti Award in the Economics, Administration and Business Area.

Professor Titular da Faculdade de Economia, Administração e Contabilidade da Universidade de São Paulo (FEA/USP) e orientador nos programas de Mestrado e Doutorado na FEA/USP e na Escola Politécnica da USP (POLI/USP). Tem Pós-Doutorado em Econometria Financeira pela Columbia University em Nova York. É Livre-Docente pela FEA/USP (ênfase em Métodos Quantitativos e Modelagem Econométrica). É Mestre e Doutor em Administração (ênfase em Economia das Organizações) pela FEA/USP, tendo recebido menção honrosa por ambos os trabalhos. É Engenheiro formado pela Escola Politécnica da USP e Pós-Graduado em Administração (CEAG) pela FGV/EAESP. Participou de cursos de Modelagem Econométrica na California State University e na Universidad de Salamanca, e de Cases Studies na Harvard Business School. É membro do Board of Directors do Global Business Research Committee e membro fundador da Academia Iberoamericana de Ciência dos Dados. Foi fundador e editor-chefe do International Journal of Multivariate Data Analysis. É colunista do IT Mídia IDGNow. Atua na área de Data Science, Business Analytics e Business Intelligence, com ênfase em análise de dados, modelagem multivariada, machine learning, pesquisa operacional, microeconometria e modelos matemáticos e estatísticos aplicados à gestão de riscos corporativos, desempenho organizacional e avaliação de empresas. É autor dos livros Data Science for Business and Decision Making (edições em Inglês e Coreano, 2019), Manual de Análise de Dados: Estatística e Modelagem Multivariada com Excel®, SPSS® e Stata® (2017), Análise de Dados: Modelos de Regressão com Excel®, Stata® e SPSS® (2015), Análise de Dados: Técnicas Multivariadas Exploratórias com SPSS® e Stata® (2015), Métodos Quantitativos com Stata® (2014), Pesquisa Operacional para Cursos de Engenharia (2013), Pesquisa Operacional para Cursos de Administração, Contabilidade e Economia (2012) e Análise de Dados: Modelagem Multivariada para Tomada de Decisões (2009), e coautor de Contemporary Studies in Economics and Financial Analysis (2011) e Trends in International Trade Issues (2006). É professor credenciado em nível global pela StataCorp e pela Timberlake. Estima e implementa modelos com uso de ferramentas de Machine Learning, Big Data e Analytics para Tomada de Decisão, como Python, R, Stata, SAS, IBM SPSS e Minitab. É autor do OVERDISP Package para o Stata, módulo computacional registrado pela StataCorp e pelo SSC Archive (Statistical Software Components) do Boston College Department of Economics. É autor da library OVERDISP para o R, módulo computacional registrado pelo CRAN (The Comprehensive R Archive Network). É autor das libraries OVERDISP, STEPWISE, SHAPIRO_FRANCIA e VUONG_TEST para o Python, módulos computacionais registrados pelo PyPI (Python Package Index Repository of Software for the Python Programming Language). É finalista do Prêmio Jabuti na Área de Economia, Administração e Negócios.