Palestras

 

Evento: Casa da Pesquisa Operacional

Data: 13.10.2021

Palestra: Séries Temporais: Aplicações no Python com Pycaret

• Slide
• Notebook Google Colab

 

=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=

Evento: Semanas da Estatística e P.O. com R

Data: 21.09.2021

Palestra: Análise Bibliométrica com o Pacote Bibliometrix no R

Slide

Vídeo

Artigo mencionado

Echchakoui, S. Why and how to merge Scopus and Web of Science during bibliometric analysis: the case of sales force literature from 1912 to 2019. J Market Anal 8, 165–184 (2020). https://doi.org/10.1057/s41270-020-00081-9

=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=

Evento: 9a Semana Acadêmica de Administração - UnB e Congresso FACE 2021

Data: 27.09.2021

Palestra: Data Science Aplicado ao Marketing 

Slide

Indicação de Livros

Data Science

• R for Data Science - Ebook
• Python for Data Analysis - Amazon | GitHub
• Data Science from Scratch - Amazon | GitHub

Data Science Aplicado ao Marketing

• Data Science for Marketing Analytics - Amazon | Códigos
• Marketing Data Science - Amazon

Artigos

Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12), 64-73.

Hayashi, C. (1998). What is data science? Fundamental concepts and a heuristic example. In Data science, classification, and related methods (pp. 40-51). Springer, Tokyo.

Sheth, J. (2021). New areas of research in marketing strategy, consumer behavior, and marketing analytics: the future is bright. Journal of Marketing Theory and Practice, 29(1), 3-12.

Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97-121.