Building Data Science Teams

Imagine cooking a stew with a single ingredient or growing a country garden with a single type of flower. One-dimensional efforts like these yield bland and boring results. Now imagine staffing a data science team with only PhDs in machine learning. In spite of the impressive pedigree, the result would be similar: bland, boring, and, possibly worse, ineffective.

But if not just data people, then who?

Data scientist Paco Nathan answers that question and more in this video on how to build a data science team. Cited in 2015 as one of the "Top 30 People in Big Data and Analytics" by Innovation Enterprise, Nathan offers insider tips gleaned from his 30+ years in technology.

• Assess the need for a data science team: Advantages, disadvantages, and how big should it be?
• Identify internal corporate sponsors to get buy-in for the data science approach
• Manage the transition of the data science team into the organization
• Discover how to identify and hire the right people for the role
• Learn best practices for setting up, organizing, and managing the team
• Practice cultivating the team and their professional growth
• Perform team gap analysis and workflow analysis
• Absorb invaluable Dos and Don’ts