Using Agile Methodologies in Data Science

The pros and cons

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Agile is an umbrella term that refers to several methodologies that focus on being iterative and on getting tangible products and features out quickly at the end of what are often called sprints. This framework has been adapted for multiple domains, including programming and design. Similarly, data science has also benefited from taking bits and pieces from Agile concepts.

Agile in Data Science vs. Programming

Data science and software development are two very different fields. Trying to use the Agile methodology in the same way as you would on a software project for a data science project doesn’t really work. When it comes to data science, there tends to be a lot of investigation, exploration, testing, and tuning. In data science, you deal with unknown data which can lead to an unknown result. Software development, on the other hand, has structured data with known results; the programmers already know what they want to build (although their clients may not).

Benefits of Agile Methodology in Data Science

So what’s up with Agile and data science? When it comes to data science, it’s all about extracting useful information from raw data and implementing machine learning models. This process…