Here are some of the public talks I’ve given – generally around Python and Analytics. In several cases, I’ve wound up assembling additional articles / resource pages for a particular topic:

1) Building Websites using

I’ve had a “real-time experiment” going on for about six months – I used a Python web micro-framework called to develop and launch a word game solver website. This project initially started as a technical experiment (wanted to give the framework a real workout before I used it for several “real money” projects in my professional work) and has progressed into an exploration of SEO and online behavior. This presentation shared a project overview, the technical / architectural design, and lessons learned with the Atlanta PyATL group as part of our regular monthly meeting.

Other Resources


2) Analyze This: Building Web Calculators Using and Google App Engine

Slides for an upcoming talk I’m giving about using on Google App Engine.

3) Monkeys, Typewriters, and the Complete Works of Shakespeare – a Brief Introduction to List Comprehensions

A whimsical introductory walk through Python List Comprehensions, one of my favorite features of the language. Uses graduated examples to show how you can use these to reshape data and handle some non-traditional applications (HTML generation, text file parsing and analysis). We also discuss nested list comprehensions and generators.

4) Using Python For Analytics

My first formal talk at PyAtl – discussed how you could build a basic “data munging” environment to support analytics with Python and some libraries (Numpy, Pyodbc). Replicates a fair amount of basic SQL functionality (transform, aggregate, etc) but enables you to write and apply custom functions to address gaps in many of the common SQL implementations (such as calculating medians and subset statistics).


Several projects later, you realize there was a better way to do it…

1) From Steve Hanov’s excellent blog on algorithms & programming: For those of you who intend to regularly filtering large lists down to the top n items, you should probably consider using heaps which are also part of the Python standard library and will provide better performance – here’s a good article that describes how you can add this to your utility belt libraries. Heap Search Article

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