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 […]
So are there new emerging approaches that are data science native? Microsoft’s Team Data Science Process (TDSP), Domino Data Lab’s Data Science Life Cycle, and the Data Science Process Alliance’s Data Driven Scrum (DDS) are approaches that are both data science native and agile. There are pros and cons specific to each approach but they share some fundamental principles.
Can you mix and match elements of multiple project management approaches? Of course! This post explores two such general hybrid approaches for data science:agile-waterfall and research and development.
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?
How do you manage data science projects? Is it software? Is it research? Or maybe, simply magic? This four-part post is an overview 10 ways projects are or could be managed. To start, we’ll explore ad hoc project management, waterfall, and CRISP-DM.
It is ironic that data science, a field built upon rigorous scientific methodologies, has been slow to adopt much rigor from project management approaches. Rather, in data science, project management tends to be ad hoc and is often on the back-burner as a secondary consideration to technologies and algorithms. Fortunately, this is changing as organizations […]
The Product Manager Role Evolving from the consumer package goods brand manager role, the software product role has become common for software teams (mindtheproduct.com). 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. Product people who tend to be […]
Various process models and frameworks such as CRISP-DM, TDSP, Domino Data Labs Lifecycle, or Data Driven Scrum describe how to execute a data science project. While useful, such models do not explicitly explain how to communicate with stakeholders on what they care most about: what deliverables will they get through a project lifecycle. In pre-project […]
Does a single centralized data science team or several decentralized teams work better? Many organizations struggle between having a single data science “center of excellence” (sometimes known as an “Enterprise” or a “Shared Service” team), which is leveraged across the organization and having smaller teams embedded within different parts of the organization. For a small […]
Ironically, data science teams that are so intensely focused on model measurement often don’t measure their own project performance which is problematic because… …But wait! Data scientists measure all sorts of metrics. Of course, data scientists will closely monitor metrics such as RMSE, F1 scores, or correlation coefficients. Such metrics are critical to answer “How […]