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 […]
In this article, I’ll explore how to create a well-defined data science process. Specifically, I’ll explain how a team’s data science process is comprised of (1) a team’s data science life cycle framework (i.e., the team’s data science process workflow), (2) a team’s coordination framework, and (3) the integration of these two frameworks. Data Science […]
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.
While there is no standard process for a team to use when working on a data science project, CRISP-DM (CRoss-Industry Standard Process for Data Mining) is one framework that is often considered for data science projects. Perhaps because of this, there are lots of web sites describing the 6 phases of a CRISP-DM project, and […]
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 […]
Due to efforts to lower the spread of COVID-19, the vast majority of data science teams are now working remote. Unfortunately, many teams are not used to working remote and so, are not aware of the challenges that need to be considered when working in a remote data science team. Most importantly – teams need […]
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 […]