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Informative Article | Computer Engineering | India | Volume 10 Issue 11, November 2021 | Popularity: 5.5 / 10

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AI-Infused Agility: Unleashing Data Science Potential with Agile Methodologies

Sowmya Ramesh Kumar

Abstract: This paper delves into the transformative realm of Agile Data Science, a fusion of Agile methodologies with the intricacies of data-driven exploration and analysis. Acknowledging the challenges posed by the iterative nature of data science projects, the methodology champions adaptability, collaboration, and incremental value delivery. It navigates the uncertainties of experimentation, allowing teams to rapidly iterate and learn from failures, essential in the discovery process. Highlighting core differences with traditional software development, the article explores Agile planning in data science, emphasizing the celebration of failures as valuable insights. It addresses the pivotal question of the customer in data science, advocating for a nuanced understanding beyond conventional product ownership concepts. Concrete Agile Data Science practices are detailed, from backlog management and time-boxed development to daily stand-ups and retrospectives. The article also underscores the criticality of review and retrospective meetings, providing insights into their role in showcasing work, celebrating successes, and fostering a continuous improvement mindset. Balancing the benefits, challenges, and considerations of Agile Data Science, the article positions this methodology as a transformative paradigm, empowering organizations to glean meaningful insights from vast datasets with resilience, responsiveness, and a relentless pursuit of innovation. It serves as a comprehensive guide for teams navigating the dynamic landscape of data science, offering practical insights and perspectives for effective project management in this evolving field.

Keywords: Agile Data Science, Data Science Project Management, Agile Methodologies, Iterative Development, Collaboration, Continuous Improvement, Adaptability, Case Studies, Challenges, Future Directions

Edition: Volume 10 Issue 11, November 2021

Pages: 1503 - 1505

DOI: https://www.doi.org/10.21275/SR24302210704

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