Womxn Level Up
Womxn Level Up is a meetup aimed at empowering software engineers and web developers who identify as female, non-binary, and/or are from underrepresented backgrounds, through education, networking, and mentorship. While there is a plethora of amazing tech meetups available, we noticed a need for something geared towards people who identify as mid-level or have some experience already working as a programmer.
Maybe you graduated from a bootcamp a few years ago and have already had a couple jobs. Maybe you’ve been junior awhile and want to be mid-level. Maybe you’re mid-level and want to be senior. Maybe you want to be the next team lead. We want to help you get there! We intend to build out the technical component of our meetup to really challenge you and level up your skills.
Interested in attending?Join the meetup
Womxn Level Up is virtual!
Once you RSVP the Zoom link will be included in the RSVP confirmation email. Make sure to have email notifications turned on for Womxn Level Up in your Meetup settings.
From Stuck to Inspired: How to Design a Career That Fitsmore_vert
Lindsay Gordon || Career coach for analytically minded people at A Life of Options
When you feel aimless or dissatisfied in your career, it’s often because you have no idea what you’re looking for. Without a clear idea of what you want to do, you get antsy about leaving your job, focused on external measures of success and distracted by shiny jobs. How are you supposed to stop doing what you think is “right” in your career and start doing what’s right for you? If you’re not clear about what you want, there’s no way you can convey it to anyone else. Come learn how to stop falling into jobs based on circumstances and start making decisions intentionally that are right for you.
How Do Algorithms Become Biased?more_vert
Eva Sasson || Product Growth Manager at Persona
There’s bias in algorithms - how does this happen? In this talk, we walk through a step by step example of building a prediction algorithm, focused on areas where bias could be inadvertently introduced. We then look at when algorithms were used to make biased decisions, and what we can do about it.