The following is an interview by Jeff Saltz with Dr Thibaut Jombart (who is a certified DSPA Team Lead). Thibaut is an Associate Professor in Outbreak Analytics at the London School of Hygiene and Tropical / Imperial College London (LSHTM / ICL) He is also a Senior Data Scientist for the World Health Organization (WHO). […]
The importance of AI project management is highlighted by a survey showing that 85% of AI projects do not deliver on their promises to business. In other words, as noted by Forbes, most AI projects fail. An AI project is different than a typical software development project, and hence, managing an AI project is different […]
This post will explore managing analytics projects by focusing on three questions: What is the difference between a data scientist and a data analyst? What is the difference between analytics projects and data science projects? What project management / project coordination framework might be helpful to manage analytics projects? Project Management Training for Data Analytics […]
Machine Learning Project Management is of growing importance If you are leading machine learning projects, you have probably noticed (or soon will notice) that machine learning project management is of growing importance and that it would be extremely helpful to have a repeatable agile process framework that helps to ensure the team can generate useful […]
During the past few months, we conducted a poll to see what project management framework teams used to help execute their data science projects. Based on our survey of 109 respondents, nearly half of the respondents most commonly use CRISP-DM. This was followed by Scrum, Kanban and “My Own”. See results below. A quick review of the […]
A data science workflow defines the phases (or steps) in a data science project. Using a well-defined data science workflow is useful in that it provides a simple way to remind all data science team members of the work to be done to do a data science project. One way to think about the benefit […]
How do you effectively define a data science process? Conceptually, a data science process explains and defines how a team should execute a project. Having a robust, repeatable process helps to ensure that the project efficiently and effectively delivers actionable insight. In this article, I’ll explore how to create a well-defined data science process in […]
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 […]
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 […]
Based on my personal experience, below are the 8 key data science team roles to think about when building and leading a data science team. These are not in any specific order, as their importance might vary from one project to another, or from one organization to another. Furthermore, not all roles are required to […]