Liz Rush

You're sold on non-traditional developers but now that you've hired someone, how do you ensure that these novice software engineers are productive, happy team memebers without wasting time or talent? With experiences pulled from managers of non-traditional developers as well as the bootcamp grads themselves, you'll leave this session with a deeper understanding of the challenges and risks of onboarding bootcamp graduates. We'll also go over strategies for managers, lead developers, & mentors on how to cultivate your new hire and grow a culture that ensures everyone on the team thrives.

This talk will cover:

- Beyond Day One: The basics of on boarding

- Structure & Guidance: How to guide your new developer into being a self-sufficient contributor

- Mentoring: For all the skills not taught at the bootcamp

- Team Dynamics & Culture: Ensuring that the team provides a healthy, welcoming environment in which the new developer can succeed

In this end-to-end discussion about the challenges with civic data, from no-documentation & incomplete government code to figuring out how to scale data-driven SOA, we'll show you how two Ruby newbies managed to create an awesomely useful parking app in just four weeks. For those new to coding, or experienced devs looking to work with civic data, we'll show you our roadmap as well as what we learned pairing as two junior developers just starting out in the big bad world of programming.

Algorithm Development is a term that leaves even the most experienced developers intimidated. Many of us have little exposure to algorithm development and many of us have never written algorithms. With how much influence algorithms are gaining over our lives, we must be prepared to ask tough questions: who writes these algorithms and what are the ethical responsibilities? What is our responsibility as end users and developers in relation to the surge of large corporations offering the use of proprietary algorithms?

When we look at algorithm development with a critical eye, we start to notice how deeply impactful the creation of new algorithms are to us as a society. This talk will discuss the ethical & social implications of failures using examples such as computer vision & poor AI training. We'll cover the need for multiple strategies, and their tradeoffs, for accountability through marketplaces, open source, governmental oversight & ombudsmanship.