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.

In this short talk Jeremy will describe the universal pipeline for performing object detection (that is, the automatic detection of objects in digital images) in Python. This will include a discussion of various classification schemes, feature extraction methods, and their fusion in the form of deep neural networks. Demo code illustrating these concepts will be shown using the IPython notebook environment.

Deploying Django is a breeze when using Ansible. Learn a bit about the power that Ansible provides and how easy it is to get started using it!

We count on unittests failure messages to give us reasonable feedback on how to proceed with a failing test. When you're working with builtin data structures and objects, unit test feedback is usually pretty good. It helps you identify and solve the problem. In contrast, the default results you get from custom objects can be practically useless. https://gist.github.com/bionikspoon/2e434a2c193a06b0996cc98c6a604de9

JIRA is a popular issue tracking and project management software. In this talk you will learn about JIRA and how to interact with the tool using the jira-python library. Primary topics covered will include an overview of the API, creating and modifying issues, linking issues, and searching from Python.

Let's give an overview of Pycon

In this talk Ryan will give a brief overview of several python projects used by many OS X admins to provide cached update services, imaging, software deployment, and configuration management. Looking at the code in these tools can provide insight into Apple's preferences frameworks and if you are in a "DevOps" type role some of the projects discussed may help you immensely.

Brian will reveal the survey results that will help shed some light on the current and future projectile of the Python programming language. Has Python reached a peak? Will it popularity continues to rise? What are the users of Python at different levels saying about the state of Python Programming Language?

There's a lot of people newish to Python and either interested or already in a startup environment (either within a larger corporation or an actual startup). Python makes a *great* tool for that, however while there’s ways to use it to carry your work along to great success, there’s ways to wind up with such spaghetti you’re tempted to throw your hands in the air and go back to Java. The focus on the talk is how to use Python and the tools it provides to avoid the unmaintainable mess while still being able to “cut corners” to get something out the door to make your boss/investors/customers happy.

Probabilistic programming languages offer a flexible and expressive way to model data by treating random variables as first-class objects. Stan is a popular and well-supported library which allows users to write models in the Stan programming language and use MCMC methods to perform Bayesian inference. Stan itself is written in C++, and has a Python interface through the PyStan package. In this talk, I'll show off some of the capabilities of PyStan and go through a simple practical example of Bayesian inference in Python.