Data science and software engineering are similar in many ways. However, dig deeper, and you’ll find key differences in their type of work, in the professionals working in these fields, and how they execute their projects. Data Science vs. Software Engineering Fields What is Data science? If you ask five data scientists to define data […]
Agile has taken the software community by storm with Scrum and Kanban leading the way. What are these approaches and do the fit in the world of data science?
What is Kanban? Kanban, which literally means billboard in Japanese, started as a supply chain and inventory control system for Toyota manufacturing in the 1940s to minimize work in progress and to match the supply of automotive parts with demand. Other industries including software have since adopted Kanban. Kanban starts with a list of potential […]
Scrum is an agile collaboration framework that can help teams increase the agility of their data science projects. Co-founded by Jeff Sutherland and Ken Schwaber in the 1990s, Scrum has become the most commonly used agile approach with over 12 million practitioners (scrum.org). Although heavily adopted in software, Scrum is also used across a wide variety […]
There are three key concepts that should be followed within an agile data science effort – use iterations, keep the iteration as small as possible and get feedback on each iteration. In other words, while there are several alternative data science workflow frameworks (sometimes known as data science life cycle frameworks), to achieve agility, agile teams should execute an […]