Statistics

Datacast Episode 11: Psychology and Neuroscience in Data Science with Francisco Carrera Arias

Datacast Episode 11: Psychology and Neuroscience in Data Science with Francisco Carrera Arias

Francisco Carrera Arias, B.S. is currently a data scientist/analyst for MotionPoint Corporation and a research assistant for the Clinical Systems Biology group at Nova Southeastern University. His current work entails performing a variety of data analyses to better inform business decisions as well as using discrete logic to analyze complex biological regulatory networks for the purposes of identifying and simulating treatment courses for chronic illnesses such as Gulf War Illness.

Datacast Episode 5: Applied Statistics in Data Science with Christopher Peters

Datacast Episode 5: Applied Statistics in Data Science with Christopher Peters

Christopher Peters is a full-stack data scientist at Zapier. He was both Zapier and Treehouse's first data scientist. Prior to his work as a data scientist, he was a research associate at LSU’s Center for Energy Studies where he was an energy economist. He has a real passion for working with, sharing, visualizing and analyzing data of all kinds using statistical, visual and machine learning techniques.

16 Useful Advice for Aspiring Data Scientists

16 Useful Advice for Aspiring Data Scientists

Data Scientists at Work displays how some of the world’s top data scientists work across a dizzyingly wide variety of industries and applications — each leveraging her own blend of domain expertise, statistics, and computer science to create tremendous value and impact.

The 10 Statistical Techniques Data Scientists Need to Master

The 10 Statistical Techniques Data Scientists Need to Master

Data scientists live at the intersection of coding, statistics, and critical thinking. As Josh Wills put it, “data scientist is a person who is better at statistics than any programmer and better at programming than any statistician.” I personally know too many software engineers looking to transition into data scientist and blindly utilizing machine learning frameworks such as TensorFlow or Apache Spark to their data without a thorough understanding of statistical theories behind them. So comes the study of statistical learning, a theoretical framework for machine learning drawing from the fields of statistics and functional analysis.