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 […]
As organizations continue to expand their data analysis competencies, the data science function is becoming more of a team sport with numerous team roles within the data science group. Of all these roles, the difference between the data analyst and data scientist role is perhaps the most confusing. Indeed, the roles have similar responsibilities. Data […]
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 […]
It’s an understatement that great leadership is challenging and rare. And leading data science teams has unique challenges: Stakeholders might get disillusioned by your team’s inability to deliver magic The battle to recruit and retain data science talent is fierce Data science’s ethical dilemmas are particularly perplexing There is not an agreed-upon process for managing […]
The battle between Data Science vs Software Engineering isn’t really a battle. Rather these are two somewhat overlapping and complementary fields that are similar in many ways. However, dig deeper, and you’ll find key differences: Data science is more exploratory. Software engineers are more focused on systems building. And data science project management should be […]
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 […]
Unlike more tactical roles like the project manager who oversees the project life cycle or a process master who drives effective processes, the 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. Evolving from […]