We are all on a Python learning path somewhere. For some, finding the right path to set out on is hard; others have been on the trail so long we forget to look around at our surroundings. In all cases, we should be mindful about how and what we learn In this talk we'll cover tips and tools for each stage in your development as a Python programmer including some live examples. How to set goals, how to find mentorship experiences, how to grow technically and how to grow interpersonally. Something for everyone!

We will be preparing the famous XOR example or one of the staples of non-linearly separable feature spaces. We will use the classic techniques like tensorflow and keras. We will also check out some of the newer examples like caffe, mxnet, pytorch, deeplearning4j, and many others. Aprons will be provided. No prior experience cooking necessary.

I first encountered SQLAlchemy several years ago. I didn't get it. It seemed every line I attempted to write would drop me into 50 tabs of labarynthine documentation. Why do we have the ORM *and* Core? Should I build my tables as `Table` instances or should I be extending `Base`? How is `Base` more declarative than a function that returns `Table`s?? Can I please just write SQL??? :sob: I'm still hesitant to peek too far behind the curtain, but I do think I've finally wrapped my head around the philosophical underpinnings of the library and the different problems SQLAlchemy allows us to solve. After all, who among us works with databases that aren't problems in and of themselves?

Katie Simmons, a data engineer at ActiveCampaign, will speak about the challenges and benefits of using Airflow for ETL at a rapidly growing company. ActiveCampaign has many thousands of databases - some including tables with up to a trillion rows - several APIs and new source requests coming in every week. This lightning talk will be an overview of using Airflow to extract, load and transform that data into our data lake so that it can be used for Business Intelligence and Data Science.

One of the most common languages used by Python developers is some shell script (sh, bash, cmd.exe, or PowerShell), but why torture yourself with poor design decisions from the 70s when you know Python?

Failure can be scary. There are real costs to a company and its users when software crashes, models are inaccurate, or when systems go down. The emotional stakes feel high-- no one wants to be responsible for a failure. We can lower the stakes by creating spaces to learn from failures, and minimize their impact. This talk introduces two ways to address failure: blameless post-mortems, to learn from an incident; and pre-mortems, to identify modes of failure upfront.

Daily fantasy sports has become a booming industry. One leader in the space is Fanduel who after raising $275 million in their series E funding, brought their valuation to over $1 billion. As the popularity of daily fantasy sports has taken off, critics have argued that success is the result of luck; yet the consistent success of a select group of players would suggest that success can be driven by skill. This talk, will go over how one could use Python to level the playing field.

An overview of Strata Data Science and python use cases inside the company.

Not all data is easily accessible. Taking info from a website that requires authentication, interaction, or even just to load a fancy script. This talk will discuss using Selenium to level up your web scraping skills, with examples and suggested practices.

In 2014 Brett Cannon summed it up when he said of Python, "I came for the language, but I stayed for the community." These are some reflections on what makes Python communities so special, the challenges they face, and how to take part in them and help them (and ourselves) grow and thrive.