If you keep up with the latest trends in the business world, then Data Science is a term that appears frequently nowadays. It is a steadily growing field and newer developments keep occurring as well. Data Science is responsible for multiple benefits for varying business industries. Small and large businesses alike are catching up; discovering high potential for growth using data analytics.
Businesses that seek to improve their solutions for customers by improving their operations, services, and products invest in Data Science projects. It is imperative to establish an expert Data Science team to streamline these projects and earn the best results.
Also known as Agile development or Agile software development, this methodology is a comprehensive term for certain development methodologies. The popularly include Scrum, Extreme Programming (XP), Crystal, Lean Development, Dynamic Systems Development Method (DSDM), and Feature-driven Development (FDD).
These methodologies each have unique approach towards problem solving. However, they share a common feature due to which they are collectively considered under Agile development. These methodologies all use the concept of iteration and constant feedback in order to refine a system under development.
Data Science is beautiful in its application and the benefits of the process are plenty. However, problem solving with Data Science is not as easy as it may seem despite experts handling the projects. The reason is that most Data Science projects don’t have an element of certainty.
It is easy to assume that you know the problem and that other people have worked on similar projects to find good results. However, things usually don’t move along as you may have planned. Moreover, you also don’t know how to schedule the project because it is impossible to determine a specific timeline.
This is why Agile methodologies have been introduced for applying on Data Science projects. While Agile has been used for software development in the past, it has been realized that it could be quite effective for refining Data Science projects as well. It is true that applying these methodologies to a data problem is different than applying to a software problem. However, creativity is needed in both situations. On the other hand, the benefit remains the same. Agile methodologies make your work easier and organized as you can use cycles. With each cycle, you can learn something new, get more refined results, and share them with other invested entities.
Agile methodologies are expected to become more common for Data Science projects in the near future. Many data scientists have reported that it makes them more productive. It obviously does not increase or decrease the skill of data scientist. However, it can help them optimize their projects. Instead of spending time on models that are unlikely to reveal any productive results, it is better to spend that time for other result-driven purposes.
When using Agile methodologies for Data Science, the focus is not on what to do but how to think. Experts believe that a Data Scientist should have an active and dynamic approach when applying Agile methodologies.
While the Agile methodologies being used for Data Science are the same as those used for software development, the approach is unique. Here is how Agile Data Science works:
Data Science with Agile methodologies is a process that also includes defining goals and following the critical path to achieve them. The analysis during the process should be continuously documented instead of focusing just on the end product in order to climb the data-value pyramid. The points defined above should be used collectively for ensuring an Agile Data Science project.