A quick overview through the components that make up Django REST Framework with a dive into a sample project. Video Link: <>

Ben will be talking about his experience getting started with Python Data Science using pandas and sci-kit learn, with Don's assistance, via the Chipy mentoring pilot program. Don is an Independent Technology Consultant, iPhone Developer and Software Architect and currently consulting with clients using Hadoop. Ben is a Senior Business Development Manager at Orbitz Worldwide and is a self taught programmer. Don is @dondrake on Twitter and Ben can be reached at https://www.linkedin.com/in/reidbenj

Mentors can be incredibly valuable in helping understand software. I want to share some of the insights I have learned from my mentorship. I believe there are important lessons to be learned from mentors that can make programming a much better experience.

Chris and Rahul would be talking about making RESTful API with Python. Chris was an Associate Writer at Ars Technica and is currently a Senior Systems Engineer at Vokal. Rahul is pursuing his MS in Computer Science at Illinois Institute of Technology. Chris is @foresmac on Twitter and Rahul can be reached at https://www.linkedin.com/in/rahul013k

We walk through the architecture, development process, and a few gotchas of deploying a web application on Heroku using their free Postgresql instance, and the Python libraries 'flask' and 'pg8000'

Tom Yarrish and Paul Ebreo will talk about their experience of the 12 week mentorship program. They will talk about what went well and what went not-so well. They will share what they learned and give tips and tricks for a successful mentor/mentee relationship. Paul is very passionate about programming, software testing, open hardware and teaching and Tom is a Digital Forensic Analyst and teaches at Loyola University.

We'll go over how to set up a daemon for mining public data using tornado, then loading that data into some web based charts.

Wouldn't it be awesome if ChiPy wrote its own book? We'd be able to get BEvERages for weeks, maybe months on the royalty! If so, we'd need to see what's already out there. This lightning talk takes a look at O'Reilly's Python books using requests and BeautifulSoup, with a little of scipy's hierarchical clustering on the book descriptions. It is presented in an iPython notebook.

Fitness tracking is great for calories and steps, but similar sensors are capable of reporting much more about how we move throughout the day. This is especially important in assessing the quality of movement for those with limited mobility. Doctors often want to know more detail about patient behavior after therapy to select and adjust the appropriate intervention. Using machine learning on wearable accelerometer signals, we estimate the activities patients with incomplete spinal cord injury are performing. By combining windowed classifier estimates over time using a hidden markov model, we show how error rates can be significantly decreased, which brings more detailed assessments of patient activity closer to a clinical reality.