Unlike more tactical roles like the project manager who oversees the project life cycle or a process master who drives effective processes, the data science product manager is more strategic. Wearing many different hats, this product person researches market needs, defines how to map the solution to the problem space, and sets the product vision. […]
While the potential ethical issues that might arise when using data science and artificial intelligence have certainly been in the popular press recently, there is not been as much discussion with respect to how a data science team should incorporate ethics within a project. Furthermore, despite this recent publicity, via a recent survey of data […]
Traditional software approaches favor developing software layer-by-layer (horizontal slicing) while software agilists strive to deliver software by thin end-to-end value streams (vertical slicing). …but what makes sense for data science? Consider a churn project… Imagine that you are tasked to pro-actively minimize customer churn at a telecom company. The retention department has requested the following […]
Data science projects are challenging. To increase your odds of success, start them by asking several key data science project questions. As Ben Franklin said, “an ounce of preparation saves a pound of headaches in your data science projects”.* To help you prepare for a project and evaluate whether to proceed, be sure you can […]
