The expansion of Data Science projects in organizations has been led by three factors: the growth in the amount of data generated, the evolution in storage capacity, and the increase in computational capabilities. However, most of these projects fail to deliver the expected value: 82% of the teams do not use any process model. Despite the popularity of Agile Methods, their adoption in Data Science projects is still scarce. Most of the existing research focuses on algorithms. There is a lack of studies on agility in Data Science. This Systematic Literature Review (SLR) was performed to identify and evaluate 16 studies that can answer how to adapt and apply CRISP-DM using different approaches — methods, frameworks, or process models. In addition, it shows how CRISP-DM has evolved over the last few decades, with derivations emerging from rigid processes to agile methods. This research then analyzes the 16 tailored models and examines the similarities and differences between CRISP-DM derivatives. As a result, it summarizes the CRISP-DM adaptation patters identified, such as phase addition, phase modification, features and tools addition, and integration with other approaches. Consequently, this SLR showcases how CRISP-DM is a robust, flexible, and highly adaptable model that can be extended to different business domains. Finally, it proposes a theoretical guide to modify and customize CRISP-DM for Data Science projects.

Data science in the healthcare sector is rapidly growing and attracting significant investments. Over the past 10 years, the number of research and publications on artificial intelligence has tripled. However, many data science projects fail due to poor planning. Canvas models emerge as effective tools for planning. A systematic review was conducted to analyze canvas models tailored for data science and focused on healthcare research. This analysis uncovered gaps in the models, particularly in addressing essential issues like ethics, security, data preservation, and sharing. We identified opportunities to enhance the models by integrating project management elements and the Data Management Plan (DMP) to address these shortcomings.

This study explores the integration of eXtreme Programming (XP) and the Cross-Industry Standard Process for Data Mining (CRISP-DM) in agile Data Science projects. We conducted a case study at the e-commerce company Elo7 to answer the research question: How can the agility of the XP method be integrated with CRISP-DM in Data Science projects? Data was collected through interviews and questionnaires with a Data Science team consisting of data scientists, ML engineers, and data product managers. The results show that 86% of the team frequently or always applies CRISP-DM, while 71% adopt XP practices in their projects. Furthermore, the study demonstrates that it is possible to combine CRISP-DM with XP in Data Science projects, providing a structured and collaborative approach. Finally, the study generated improvement recommendations for the company.

Healthcare data preparation challenges often arise from its complexity and heterogeneity. This study introduces a framework for preparing structured and non-temporal healthcare data, tailored for unsupervised learning models. The HealthDataPrep framework extends CRISP-DM, guiding data preparation in four key areas: data selection, data cleaning, feature engineering, and data privacy. Validated using a dataset on Alzheimer's disease mortality in Brazil, the framework generated two datasets utilized in unsupervised algorithms, K-Modes for clustering and Apriori for association rules. Results revealed sociodemographic profiles vulnerable to the disease, predominantly featuring Alzheimer's mortality in women over 80, widowed, and with low education in the Southeast region. Furthermore, the study identified associated causes of Alzheimer's mortality, such as pneumonia, respiratory failure, and sepsis. These insights are crucial for shaping public policies and intervention programs aimed at reducing Alzheimer's mortality. The research contributes methodologically to healthcare data preparation and enhances our understanding of Alzheimer's mortality in Brazil.

A ansiedade tornou-se uma preocupação crescente, especialmente após a pandemia de COVID-19, que elevou os casos em 25% no mundo e a consolidou como a quarta maior causa de afastamento do trabalho no Brasil. Este estudo de abordagem quantitativa analisou as notificações de transtornos de ansiedade relacionadas ao trabalho no estado de São Paulo, com foco nas mudanças pós-pandemia. Utilizando análise de tendência, regressão linear e aprendizado de máquina não supervisionada (clusterização e regras de associação), o estudo explorou o crescimento, a mudança de padrão nas notificações e os perfis demográficos e comportamentais dos trabalhadores afetados no período de 2020 a 2023. Os resultados identificaram quatro perfis principais de indivíduos com transtorno de ansiedade. O maior cluster foi composto por trabalhadores de 30 a 39 anos, atuantes nos setores de comércio, seguido por um cluster de trabalhadores do setor administrativo com idade entre 40 a 49 anos. Embora, em sua maioria, não usuários de drogas psicoativas, álcool ou tabaco, muitos já utilizaram psicotrópicos e substâncias psicoativas e/ou psicofármacos, o que foi associado à incapacidade temporária. Assim, políticas de prevenção e suporte psicológico são essenciais para o bem-estar e a produtividade no ambiente de trabalho.