Python Mentors Lightning Talk – Chris & Rahul
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
Example app using Flask and pg8000 (Postgres) on Heroku
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'
Being A Mentee In The ChiPy Mentorship
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.
Python Data Science 101 - how mentoring helped me get from raw data to SKLearn by Ben Reid
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
ChiPy Mentorship 7-Minute Retrospective
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.
MM - Japhy/Sebastian - Mining and charting
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.
92 Python enthusiasts attended this meeting.
51 Python enthusiasts attended this meeting.
Python For Humans
A lightning look at O'Reilly's Python books
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.
59 Python enthusiasts attended this meeting.
Hidden Markov Models to improve activity recognition in patients with spinal cord injury
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.
Innate learning: training the brain before the eyes open
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.
47 Python enthusiasts attended this meeting.
Data Science Pipeline in Python
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.
Write Pretty Code
Journey into the world of poorly formatted code to beautiful written pep8 styled goodness.
78 Python enthusiasts attended this meeting.