2025 FWS Data Management Workshop

This course will cover the fundamentals of Lean, Six Sigma, Theory of Constraints and Continuous Process Improvement (CPI).  

Innovation through CPI is a key skill for effective program and project management and ensuring products remain on time and on budget. These methodologies are applied to risk management, data management, performance improvement, and evidence-based decision making.

The course will be led by Billy Skaradek, a Lean Six Sigma Black Belt with numerous federal process improvements to his credit.

Learning Objectives: Upon completion, you will:

• Understand the key concepts of Lean, Six Sigma, Theory of Constraints, and CPI.
• Have a rudimentary understanding of some tools used in improving processes.
• Assist with CPI process improvement projects as a sponsor or subject matter expert.
• Assist with identification of improvement opportunities.

Target Audience: FWS employees that manage or use routine processes, want to improve efficiency and effectiveness, and are challenged to “do more with less”.

Tidy Data - A Strategy & Mindset - Bonnie Campbell 
FWS gathers a tsunami of data daily. To be usable this data must first be tidied. Tidying allows us to process data more efficiently for understanding and use. This course aims to encourage consideration and use of the tidy data strategy & mindset for the data we use in FWS.

Combines presentation & hands on activities.

Learning Objectives:

• Recognize the difference between tidy & messy data
• Identify & fix issues that make data messy vs. tidy
• Add tools to your quiver for making data tidy

Learn about DOI GitHub Enterprise Cloud (DGEC), related policies, its origin and implementation. DGEC features, access requirements, collaboration, tie-in with code.gov and more. Combines presentation & hands on activities. Learning Objectives: + Know why DGEC is important for your work + Know where to go for DGEC information & support + Know how to obtain access, establish repositories, leverage features + Become familiar with internal & sponsored collaborators workflows Prerequisites: None. Familiarity with code development (including versioning & control) Git, GitHub is a plus. Lab computers require: Git, R, RStudio