Combining data science process research with industry leading agile training, the Data Science Process Alliance (DSPA) advises individuals and organizations on how to effectively execute data science projects and is the leading data science process membership, training and certification organization.
In short, DSPA’s focus on helping people improve their Data Science Process so they can improve data science project outcomes (as well as improving their career).
Explore DSPA’s goals, people, our talks and published research papers.
Goals of the Alliance
DSPA is the world’s leading authority on how a team should effectively execute a data science project and is the only data science process professional membership and certification organization.
The data science process alliance provides education, certification, and the dissemination of research to support data science practitioners and researchers improve data science project outcomes via the use of appropriate and effective data science process frameworks.
In short, to help organizations transform the way data science project are executed, DSPA aims to:
Inspire individuals, leaders, and organizations to adopt improved data science process frameworks. We support their transformations with training, certification and research of data science process best practices.
Enable improved data science team process via learning that is available via face-to-face courses, online courses, webinars, and the dissemination of research via our web site.
Guide the application of useful data science process practices, principles, and values through our career-long certification path. Our community of coaches and trainers are focused on providing knowledge, skills, and experience that support transformation of individuals and organizations.
Jeff Saltz, since joining Syracuse University in 2014, has focused on how to effectively manage and coordinate data science projects. 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.
Alex Sutherland has been building predictive analytical solutions for 20+ years. He has also been using and teaching/coaching Agile frameworks for 15+ years.
Alex started his analytical work 20 years ago by defining, building and releasing a world-leading analytical solver for Poker (as well as studying Game Theory at Caltech). For the past 15 years, in addition to his data science work, Alex as also held numerous leadership positions at ScrumInc (world’s leading authority on Scrum), which founded by Jeff Sutherland – the co-creator of Scrum. For example, Alex was part of ScrumInc’s first Scrum Master class effort (in 2007). Alex has also helped define new agile frameworks (such as Scrum@Scale), and the training/certification for those frameworks.