In this audio-only special Becoming a Data Scientist Podcast episode, I interview Dr. Ed Felten, Deputy U.S. Chief Technology Officer, about the Future of Artificial Intelligence (from The White House!).
Tag: machine learning
Becoming a Data Scientist Podcast Episode 09: Justin Kiggins
Justin Kiggins, who calls himself a “full stack neuroscientist” talks to Renee about how he started as a musician majoring in music therapy, switched to mechanical engineering, and eventually made his way via biomedical engineering and neuroscience to study auditory perception and the brains of communicating birds.
Podcast Audio Links:
Link to podcast Episode 9 audio
Podcast’s RSS feed for podcast subscription apps
Podcast on Stitcher
Podcast on iTunes
Podcast Video Playlist:
Becoming a Data Scientist Podcast Episode 08: Sebastian Raschka
Renee interviews computational biologist, author, data scientist, and Michigan State PhD candidate Sebastian Raschka about how he became a data scientist, his current research, and about his book Python Machine Learning. In the audio interview, Sebastian also joins us to discuss k-fold cross-validation for our model evaluation Data Science Learning Club activity.
Podcast Audio Links:
Link to podcast Episode 8 audio
Podcast’s RSS feed for podcast subscription apps
Podcast on Stitcher
Podcast on iTunes
Podcast Video Playlist:
Becoming a Data Scientist Podcast Episode 05: Clare Corthell
Renee Teate interviews Clare Corthell, founding partner of summer.ai and creator of the Open Source Data Science Masters curriculum, about becoming a data scientist.
Podcast Audio Links:
Link to podcast Episode 5 audio
Podcast’s RSS feed for podcast subscription apps
A Challenge to Data Scientists
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.