DSPA Info
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
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.
Leadership
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 10+ years. He has also been a serial entrepreneur and used Scrum to launch his first company over a decade ago.
Alex also built and and released the first commercial analytical solver for Poker and studied Game Theory at Caltech. More recently, in addition to his data science work, Alex as also worked with Scrum Inc. (the world’s leading authority on Scrum, which founded by Jeff Sutherland – the co-creator of Scrum). While there, Alex contributed to defining a new agile framework, Scrum@Scale.