Version Control for Enterprise Data Science Teams

Learn best practices for tracking and organizing data science work in repositories from the experts at GitHub and

June 12, 2018 | 10:00 - 11:00 a.m. PST

About the webinar

Data scientists and machine learning engineers saw the highest rates of job growth in the country last year, but enterprise adoption of data science is, for the most part, still in its infancy. Companies with high-performing teams have recognized that hiring top talent isn’t enough — it’s imperative to have a process in place for iterating, testing, and collaborating on data science initiatives.

In this webinar, GitHub Solutions Engineer Bryan Cross and Data Scientist Tuck Ngun will demonstrate the benefits of using version control in conjunction with a data science platform to track changes and collaborate on data science and machine learning projects. This webinar will be especially valuable for data scientists and managers working closely together or in parallel on projects in an enterprise setting. You will learn best practices for:

  1. Managing code and data science projects across a large team
  2. Organizing work in repositories so that it is discoverable
  3. Using Git in conjunction with a data science platform to improve time to value
  4. Documenting analysis and testing code
  5. Collaborating effectively using software development best practices
  6. Improving reproducibility and informing future projects 
  • Date: Tuesday, June 12, 2018

  • Time: 10:00 - 11:00 a.m. PST

Register for the event

Bryan Cross
Senior Solutions Engineer, GitHub
Bryan's journey into software started nearly 30 years ago when he was a professional archaeologist working in Guam. Over the years, he's worked in every aspect of the software development, including architecting and delivering complex enterprise systems on a global scale across many business verticals. At GitHub, Bryan specializes in helping customers address the entire Software Development Lifecycle, helping large enterprises implement DevOps and DevSecOps.
Tuck Ngun
Data Scientist,
Tuck Ngun is a data scientist at, where he works on the customer success team to help companies level up their data science work. Tuck has nearly a decade of experience working with challenging datasets and using machine learning and statistical modeling techniques to build predictive models. Previously, he worked as a postdoctoral researcher at the University of California, Los Angeles, where he focused on creating predictive models of age and sexual orientation based on biological markers.