Being Agile in a Data Science Project

Author:

Cordeiro Renato,Alves Isaque,Alves Samara,Goldman Alfredo

Abstract

AbstractApplying agile practices in data science requires adaptations. This paper describes challenges and lessons learned in two applied machine learning projects developed in the XP Lab course at University of São Paulo in Brazil. It compiles six suggestions for educators and practitioners who want to bring agility to their data science initiatives.

Publisher

Springer Nature Switzerland

Reference8 articles.

1. Isaque, A., Leonardo, L., Paulo, M., Carla, R.: Product engineering for machine learning: a gray literature review. In: 2021 IEEE/ACM 43rd International Conference on Software Engineering: WAIN 2021 - 1st Workshop on AI Engineering - Software Engineering for AI

2. de Oliveira Rosa, T., Goldman, A.: Is it possible to apply agile methods to contribute to the Linux kernel?. Agile Processes Softw. Eng. Extreme Program. Workshops 396, 291–297 (2020)

3. Goldman, A., Almeida Santos, V.: Continuous Improvement of an XP Laboratory Course: An 18 year History, Experience Report, In: Agile (2019)

4. Wirth, R., Hipp, J.: CRISP-DM: towards a standard process model for data mining. In: Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining (2000)

5. Sato, D., Wilder, A., Windheuser, C.: Continuous Delivery for Machine Learning (2019). https://martinfowler.com/articles/cd4ml.html

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