Abstract: 

In this talk, I want to share my observations and suggestions on the topic of bringing everyone onboard of the data science/AI era and creating cohesive common grounds in data-centered conversations.

There is a big hurdle for people who wish to join the data science domain or even understand what’s going on in the field without a STEM background. As a data scientist as well as a data science instructor, I have seen students from various backgrounds and got a chance to know their difficulties and career shift stories. This talk was impossible before I actually taught data science because the mindsets or even vocabulary for me, with two degrees in STEM, are so different from my students in many ways.

Three topics will be discussed. First, I am going to address the biggest challenges for people who shift careers to data science. My original thought is the knowledge itself, it turns out not really. The second topic is about how to teach and mentor. This topic is particularly interesting because it not only applies to teaching a specific topic but to general mentoring and instructing. It is about fundamental principles in communication and conveying technical ideas. The last topic is about job hunting. Some of my students and mentees succeed in landing a job in a quick fashion but some don’t. I am not trying to draw a conclusion with statistical significance but only share some key characteristics that, in my opinion, boost the last stage of career shifts.

If data science/AI is a pool, attendants of the conference are all experienced swimmers, I would love to take this opportunity to direct some attention to the group of people who start learning to swim after their 20s. This is important because the knowledge gap creates unnecessary boundaries and distrust while AI should be for all.

Bio: 

Ron Li is a data science instructor and senior data scientist at Galvanize, Inc. Before that, He worked on machine learning and knowledge graphs at the Information Sciences Institute. Ron has published a 4.5-star rating book Essential Statistics for Non-STEM Data Analysts. He has also authored/co-authored several academic papers, taught data science to non-STEM professionals as pro bono service, and gave talks at conferences like PyData.