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.

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.

Amorphous, blob-like patterns of neural activity form and move over the eye during visual development in animals. Why do such patterns exist? We show that these patterns are this way to better prepare the visual system for natural vision. Essentially, these are movies played in the eyes to refine the visual system before the eyes even open. We use python to model the developing visual system, produce an efficient code based on those patterns, and show how that code matches what is seen biologically. In this way, we show that during your early development you are learning from innately generated patterns - a unique twist in the debates of nature and nurture.

In my view, the core of Data Science is the development of predictive models (recommendation engines, fraud detection, churn prediction, etc.). While predictive models can be built in a number of languages I choose to do my work in Python because the Data Science Pipeline is more than just building models. I'll talk about the larger model development process and how I use Python to automate and document my work.

Journey into the world of poorly formatted code to beautiful written pep8 styled goodness.

Robot framework (robotframework.org) is an automated acceptance testing framework written in python. It can be used for a wide range of testing activities, from web, mobile and desktop UI testing, to database testing, RESTful and SOAP services, and much more. Bryan will give a brief overview, do some demonstrations, and answer questions.

Young, low-income, first-time mothers and their babies often face dramatically increased risks to their health, education, and economic self-sufficiency. Nurse-Family Partnership (NFP), a national nonprofit organization, intervenes by pairing these mothers with specially-trained, registered nurses. Expectant mothers receive regular home visits from pregnancy until the baby is two years old. The result: healthier pregnancies, more stable families, and better developmental outcomes for children.

The maternal deaths in Mexico from pregnancy, childbirth or postpartum complications have decreased from 89 deaths per 100,000 live births in 1990 to 43 in 2011. Despite this improvement, the rate of decline has significantly slowed and Mexico is not on track to achieve its Millennium Development Goal of reducing maternal mortality 75% by 2015.