I just got back from PydataDC, where I learned a lot, had fun, and met a bunch of awesome people! I’ll definitely write about it more later, but I wanted to share my slides here since I told the attendees they could find them on my website. I got good feedback on the talk, and I’m so glad that my message resonated with some people! The talk was recorded and video should be out within a few weeks! Here are the slides: Becoming a Data Scientist – Advice from my Podcast Guests and the slide notes. Update 10/26: Here is the recording of my talk, with a playlist of other talks from PyData...
In this interview, we meet physicist Debbie Berebichez, who you might recognize from her TEDx talks, her appearances in Discovery Channel’s Outrageous Acts of Science and other TV shows! Debbie grew up in Mexico City and was discouraged by her family and teachers from studying science, but later went on to become the first Mexican woman to get a PhD in physics from Stanford, and is now Chief Data Scientist at Metis Data Science Bootcamp in New York.
Verena, David, Kerry, and Anthony are members of the Becoming a Data Scientist Podcast Data Science Learning Club! They appear in the order in which they joined the club, and each discuss their starting points before joining, their participation in the activities, and advice they have for new data science learners.
Podcast Video Playlist:
Youtube playlist of interview videos
More about the Data Science Learning Club:
Data Science Learning Club Welcome Message
Note: The video is the interview only. The audio podcast has the intro, interview, and data science learning club activity explanation.
In this episode we meet Will Kurt, who talks about his path from English & Literature and Library & Information Science degrees to becoming the Lead Data Scientist at KISSmetrics. He also tells us about his probability blog, Count Bayesie, and I introduce Data Science Learning Club Activity 1. Will has some great advice for people learning data science!
As data scientists, we are aware that bias exists in the world. We read up on stories about how cognitive biases can affect decision-making. We know that, for instance, a resume with a white-sounding name will receive a different response than the same resume with a black-sounding name, and that writers of performance reviews use different language to describe contributions by women and men in the workplace. We read stories in the news about ageism in healthcare and racism in mortgage lending.
Data scientists are problem solvers at heart, and we love our data and our algorithms that sometimes seem to work like magic, so we may be inclined to try to solve these problems stemming from human bias by turning the decisions over to machines. Most people seem to believe that machines are less biased and more pure in their decision-making – that the data tells the truth, that the machines won’t discriminate.
When I decided that I wanted to become a data scientist, I started following some data scientists on twitter to see what they talk about and what was going on in the “industry”. Then I saw them pointing one another at resources, and answering each other’s questions, and I realized I had only seen the tip of the iceberg of “Data Science Twitter”. That’s when I created a new twitter account.