Agile Data Science: Exploring a Framework to Help a Team Generate Actionable Insight
Agile Data Science: Exploring a Framework to Help a Team Generate Actionable Insight

Abstract: 

Data scientists and data science managers need to think through many questions relating to how to best execute their data science efforts. While many challenges are technical in nature, others are focused on how an extended team should work together. For example, what is the most effective way to work with stakeholders? How should the team validate the results?

This workshop will provide an agile data science framework that data scientists, data science team leads and data science clients can use to help ensure a successful data science project. The focus of this framework is not on which specific algorithm a team should use, but rather, how to ensure that the data science team is progressing effectively and efficiently.

Key aspects of the framework, that will be discussed, include:
Forming Data Science Teams:
1. Staffing the team
2. Roles on Data Science Projects
3. Training data science teams
4. The challenge coordinating data science functions/capabilities - Coordinating IT, analytic and client teams

Agile Data Science
1. Key Tenants of Agility
2. Benefits of using an Agile approach
3. Agile data science vs Agile software development

Pros and Cons of Existing Frameworks
1. CRISP-DM
2. TDSP
3. Scrum
4. Kanban

Establishing an Organization’s Agile Data Science Framework
1. An Agile data science process methodology
2. Balancing “research” vs “getting something useful”
3. The Analytic life-cycle

Bio: 

Since joining Syracuse University in 2014, Jeff has focused on how to effectively manage and coordinate data science projects within and across teams. Via his research and consulting, Jeff has become an expert in this field, having worked across a range of organizations and published 30+ peer-reviewed academic papers that (1) explore the challenges in executing data science projects, and (2) evaluate potential frameworks via experiments and real-world case studies.

Prior to joining Syracuse University and consulting on data science efforts, Jeff worked at JPMorgan Chase, where he reported to the firm's Global Chief Information Officer and drove technology innovation across the organization, while also having other leadership roles within the bank, such as leading the IT effort for credit risk analytics within the consumer bank.