Geek Professor Drops Rap Video:
"Predict This!" by Dr. Data
"Predict This!" by Dr. Data
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ATTEND THE GEEK'S CONFERENCE:
Rap song: "Predict This!"
Educational topic: Predictive analytics
Artist: Dr. Data (Eric Siegel, Ph.D.)
MP3 downloads: Rap and elevator music
With Chinese subtitles: Facebook or China's QQ
Behind-the-scenes: Watch video outtakes
Description: The USA's Chief Data Scientist designated his profession "the sexiest job of the 21st century." Here's proof he was right. Watch the origin story of Dr. Data, who performs a catchy infotainment rap about predictive analytics and reveals the power of embracing your true inner geek.
Read more: Geek interview and press release
Sample lyrics (click here for the full lyrics):
Predictive analytics learns from the data you supply,
and predicts if you will click, buy, lie, or die.
It ain't astrological - it's math, it's methodological.
So better pay attention cause my flow is pedagogical.
Music video credits:
Directed by: Laura Lee Bahr
Produced by: Ezra Werb and Laura Lee Bahr
Edited by: Ezra Werb
Costume design by: Maria Callou
Song written by: Eric Siegel
Song produced by: Neil Cleary
Vocals: Eric Siegel with Laura Lee Bahr and Neil Cleary
Elevator music version: Hughie Stone Fish
Director of photography: Rachel Elias
Gaffer: Karl Coleman
Grip/Electric: Aaron Pong, Jon Acevedo, Ian Tindell
Production assistant: Mikaela Runyan
International cities camerawork by: Maria Callou, Chrissy Curran, John Fitch, Efthalia Papacosta, Carol Robins, Fiona Robins, Jason Rajasinghe, Lisa Schamberg, and Gabriella Stromhielm.
Post effects by: Todd Lewis Graphic design for faux magazine cover: Matt Kornhaas
Choreography: Aubre Hill
Hair and makeup: Karen Huitsing
Headshot photo for faux magazine cover: Dana Patrick
Data scientist: Eric Siegel
Dr. Data: Eric Siegel
Robot dancer 1: Claudine Quadrat
Robot dancer 2: Mariam Karkashadze
Ingenue: Laura Lee Bahr
Nerds who become geeks and dance: Maria Callou, Erin Cowan, Charlene Dipaola, Rachel Elias, Nicolas Frantela, Omar Njie, Mikaela Runyan, Elinor Shapiro, Danica Siegel, Karen Zumsteg.
Shot on location in Los Angeles, South Africa, Antarctica, Singapore, Burning Man (Nevada), Hawaii, Sri Lanka, New York City, Rome, and Athens.
Special thanks also to Dean Abbott, Angela Robins, John Elder, Matthew Finlay, Adam Kahn, Ene Piirak, Lisa Schamberg, and Andrew Siegel.
A Prediction Impact, Inc. video.
Here's a little song about predictive analytics. If you wanna learn, you gotta listen to the lyrics. Listen to it twice; maybe you can memorize it. It goes a little somethin' like this. CHORUS: I can predict your every move; just gimme all your information. Provide me the data to improve and I'll apply the computation. People don't dig data - "Boring!"'s the sentiment. Crapped out by technology - electronic excrement. But my theory is that data is experience - serious! - and deep inside the data is the key, see. Predictive analytics learns from the data you supply, and predicts if you will click, buy, lie, or die. It ain't astrological - it's math, it's methodological. So better pay attention cause my flow is pedagogical. CHORUS Predictive analytics can help you with decisions; you can call, mail, credit, or hire with precision. On law, love, and life, you can prognosticate whom to investigate, incarcerate, set up on a date, or medicate. Charlie Brown never gets his kicks; that's why every old dog needs a brand new trick. If you get sick of chasing sticks or clicks with just a quick fix, you need to learn to predict. CHORUS BRIDGE: Anticipating my mood - boy, that's totally rude 'cause you would predict anything that moves. But now I see whatcha mean - I could have never foreseen data scientists are the sexiest geeks! The method is to modify your model incrementally. Tweak the technique, geek, improving it incessantly. The model is trained; machine learning is attained. Make it rain... pop the Champagne! If it fails, the model bit it; simply put, it overfit it. Take a minute... flip your bits and quit it. To fix risk and quickly pick Netflix top hits that stick... you need predictive analytics. CHORUS (repeats twice)
For inquiries: email@example.com