Starting a few years ago, Capital One has committed to go all-in on public cloud and open source software for many of our core business operations, processes, and machine learning models. To support this transformation, we embarked on a multi-year journey to build a Python community with critical mass of users, and scale adoption of Python in our business analyst and data analyst workforces.
Python has been envisioned since its early days as a programming language which can be used to "create better, easier to use tools for program development and analysis", as well as "build a user community around all of the above, encouraging feedback and self-help".  In our experience scaling Python adoption amongst analyst communities within a Fortune 500 company, we have found the aforementioned visions true to form - not only is Python a great first programming language for our analysts to learn, it also comes with "batteries included" and contains many of the data-related tools and libraries which allows our analysts to get productive very quickly.
This talk will highlight our multi-pronged approaches to overcome organizational inertia to build a community of Python users, provide Python and OSS training, and encourage Python adoption (with mixed success). We'll share what (we think) best practices are out there, and lessons learned along the way.
Reference:  Computer Programming for Everybody (http://www.python.org/doc/essays/cp4e.html)