Statistical analysis projects vary in complexity, ranging from a single run t-test to multi-analyst, multi-year projects with large and diverse datasets, time consuming models, frequent data/code updates, and complex reporting. Having organized systems is always a good idea— and for projects on the complex end, preparing process flow, file structure, version control and intermediate computations can help to reduce chaos and increase the likelihood of successful outcomes.
In this training, you’ll learn common ways to manage data analysis projects. We will consider process flow, file/folder structure, version control, and intermediate save points to help establish efficiency and reproducibility.
You’ll leave with a general idea of version control options and tools for complex workflow planning.
About the Instructor
Clark Kogan is an experienced statistical scientist and owner of StatsCraft LLC.
His expertise includes Bayesian models, generalized linear mixed models, research design, and R programming. As a collaborator, consultant, and mentor across multiple fields (including agriculture, veterinary medicine, psychology and pharmacy), Clark loves the challenge of finding the best statistical strategies for the specific research project. He especially enjoys the collaborative discussions that lead to insights and solutions.
Clark has a Ph.D. in Mathematics from the University of Montana, and currently serves as an adjunct faculty at Washington State University. In his prior positions, he was Associate Director of the Center for Interdisciplinary Statistical Education and Research at Washington State University and Data Scientist at Trove Predictive Data Science.
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