I was Ray's mentee during the summer of 2016. My project lets a player of Minecraft Pi 3D print an object they have built in the game. I will co-present this project with Peg Keiner, the Technology Director at GEMS World Academy in Chicago where my program was used in their elementary school classroom this Fall. I will also share some neat projects from my stint this Fall as a High School Marine Science teacher aboard the schooner Roseway during Ocean Classroom (worldoceanschool.org). I introduced students to Python and had them design and code a sensor kit with Raspberry Pi's to measure an aspect of the sailboat. The code is viewable on my github profile: https://github.com/thaneofcawdor.

I've lead a couple once-a-week, 10-week apprenticeships that allow 5th-8th grade students to explore the basics of Python through an interactive shell at their elementary school. The students primarily use lab computers, but they are also exposed to general command-line concepts through the use of several customized Linux laptops. In this talk I will discuss my goals for the students, the concepts that I introduce, how I interact with the students, some of the challenges that arise (for myself and the students), and some tips that may be helpful to other volunteers.

Discussion of common problems migrating Django application databases, particularly when switching DBMS.

In keeping with the education theme, I thought I would give a talk on the Turtle module in Python, which is more or less a clone of Logo.

Multiple Langauge competition: * JavaScript - Divya * Clojure - Cezar Jenkins * SQL - Heather White * Babbage's Analytical Engine programming cards - Phil Robare * R - Parfait * Assembly (AVR) - Nick Timkovich * Groovy - Jerry Dumblauskas * Swift - Matt Green * Julia - Andrew Webster

In this talk, I will be talking about starting, stopping, and displaying incremental data from long-running tasks in an asyncio-based web application.

This talk will cover the development cycle, build tools, and python frameworks commonly used by Telnyx Python engineers.

What's the main difference between SQLAlchemy and Django's ORM? When might a person prefer Pony ORM or peewee? -- popular Object-Relational Mapping libraries in Python are compared and contrasted.

New York State’s Department of Environmental Conservation (NYSDEC) is the regulatory agency for environmental issues in the state. Their mission is to conserve, improve and protect New York State’s natural resources and environment and to prevent, abate and control water, land and air pollution. NYSDEC currently conducts approximately 700 inspections each year of facilities in the state that manage hazardous waste. DSSG will work on more effectively allocating inspection resources by creating predictive models that identify facilities with high likelihood of violating environmental regulations. In 2015, we worked with the federal EPA targeting hazardous waste facilities subject to the Resource Conservation and Recovery Act (RCRA). With inspection data from NYSDEC and the public RCRA dataset, we will build a similar model to identify RCRA violators specifically in the New York region, as well as further explore the possibility of applying models to other compliance inspection programs, such as the Clean Air and Clean Water Acts.

Many police departments in the United States use “early intervention systems” to identify officers who may benefit from additional training, resources, or counseling. These systems attempt to determine behavioral patterns that predict a higher risk of future adverse incidents, ranging from excessive use of force and citizen complaints to on-duty accidents and personal injury. Detecting these risk factors enables departments to develop targeted interventions and make operational changes to protect officer safety and improve police/community interactions. Last summer, DSSG worked with the Charlotte-Mecklenburg Police Department on building a better early intervention system, applying data analysis to provide insights on individual and situational risk factors for adverse interactions. This year, we will partner with additional police departments, including the Metro Nashville Police Department, to test and expand this work in new municipalities, improving both the overall model and local performance. Like last year, we will use anonymized police data and contextual data about local crime and demographics to detect the factors most indicative of future issues, so that departments can provide additional support to their officers.