What is Agile Data Science?
Agile data science is a data science approach that is based on the principles of agile software development. It is designed in a way that is flexible and responsive to constantly changing requirements and priorities and emphasizes the rapid delivery of working software and data-driven insights.
Agile data science typically involves close collaboration and communication among data scientists, software developers, and business stakeholders, with a focus on delivering value to customers and users. It involves frequent iterations and rapid prototyping, with a focus on delivering working models and insights as quickly as possible.
Some key principles of agile data science include:
Collaboration and communication: Agile data science relies on close collaboration and communication among team members and stakeholders.
Flexibility and adaptability: Agile data science is designed in a way that is flexible and responsive to changing requirements and priorities.
Continuous delivery and iteration: Agile data science emphasises frequent delivery of working models and insights, with a focus on continuous improvement.
Data-driven decision-making: Agile data science approaches prioritise making data-driven decisions based on the insights and models that are developed.
Agile data science is increasingly being used in industries such as finance, healthcare, and retail to help organisations make better use of their data and drive business value.
The aspects that are hard to apply in data science are:
- Data Science activities are more poorly defined, making estimation more challenging.
- Scope and criteria are subject to rapid change.
- It is anticipated that data science sprints should produce results similar to those of engineering sprints.
- Being overly skilled or organised in Scrum.