Agile Data Science - Implementation of Agile



There are different methodologies used in the agile development process. These methodologies can be used for data science research process too.

The flowchart given below shows the different methodologies −

Types-of-Agile-Project-Management

Scrum

In software development terms, scrum means managing work with a small team and management of a particular project to reveal the strength and weaknesses of the project.

Crystal methodologies

Crystal methodologies incorporate imaginative techniques for product management and execution. With this strategy, teams can go about similar tasks in different ways. Crystal family is one of the least demanding approach to apply.

Dynamic Software Development Method

This delivery framework is basically used to implement the current knowledge system in software methodology.

Future driven development

The focus of this development life cycle is features involved in project. It works best for domain object modeling, code and highlight development for ownership.

Lean Software development

This method aims at increasing the speed of software development at low cost and focusses the team on delivering specific value to client.

 

Extreme Programming

Extreme programming is a unique software development methodology, which focusses on improving the software quality. This comes effective when the client is not sure about the functionality of any project.

Agile methodologies are taking root in data science stream and it is considered as the significant software methodology. With agile self-organizing, cross-functional teams can work together in effective manner. As mentioned there are six primary categories of agile development and each one of them can be streamed with data science as per the requirements. Data science involves an iterative process for statistical insights. Agile helps in breaking down the data science modules and helps in processing iterations and sprints in effective manner.

The process of Agile Data Science is an astonishing way of understanding how and why data science module is executed. It solves problems in creative way.





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