Overview
Agile practices have been used in software for years. They have helped engineering teams complete complex software projects and improve communication between engineers and stakeholders.
Agile isn’t limited to software development – it’s being implemented everywhere, and most enterprise data practices are areas of need. In this fast-paced world, adapting quickly is key. Data science teams can greatly benefit from the improved flexibility, collaboration and output available from adopting agile concepts and practices.
This data science course will provide you with the tools required to leverage agility to break down complex requests from management, provide more accurate timelines, and communicate the overall status of your projects to management and other stakeholders.
In This Agile Data Science Course, You Will:
- Review classic agile practices like Scrum, XP, and Kanban
- Learn how agile techniques can be applied to data science projects
- Learn how managing software projects and data science projects differ
- Learn how stories, sprints, and agile ceremonies and artifacts can be applied to data models and application integration
- Discover a toolbox of operational enablers drawn from the world of agile engineerings, such as database versioning, continuous integration, and test automation.
- Examine agile roles and how they can be applied in a data or advanced analytics practice
- Work using agile principles to address needs with faster, more nimble responses.
The course is also available on our public schedule via Live Virtual Classroom:
Contact us here.