Data Science

Datacast Episode 15: Thoughts on Data Science from the perspective of a behavioral scientist with Nick Gaylord

Datacast Episode 15: Thoughts on Data Science from the perspective of a behavioral scientist with Nick Gaylord

Nick Gaylord has worked as a data scientist in the Bay Area for about the last 5 years. Currently, he’s a member of the Johnson & Johnson Health Technology team, and prior to that, he has worked in different fields ranging from small business revenue analytics to enterprise machine-learning-as-a-service platforms. Like many data scientists, he started out as an academic before transitioning to industry, in his case earning a Ph.D. in Psycholinguistics from the University of Texas at Austin in 2013.

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.

How to Think Like a Data Scientist in 12 Steps

How to Think Like a Data Scientist in 12 Steps

At the moment, data scientists are getting a lot of attention, and as a result, books about data science are proliferating. While searching for good books about the space, it seems to me that the majority of them focus more on the tools and techniques rather than the nuanced problem-solving nature of the data science process. That is until I encountered Brian Godsey’s “Think Like a Data Scientist.” 

The 5 Machine Learning Use Cases that Optimize Your Airbnb Travel Experience

The 5 Machine Learning Use Cases that Optimize Your Airbnb Travel Experience

Wondering how Airbnb sorts and delivers its listings when you search for a place to stay on your next getaway? Check out 5 important use cases of machine learning that are currently being deployed by Airbnb’s engineers and data scientists to solve this problem.

The 5 Computer Vision Techniques That Will Change How You See The World

The 5 Computer Vision Techniques That Will Change How You See The World

Computer Vision is one of the hottest research fields within Deep Learning at the moment. It sits at the intersection of many academic subjects, such as Computer Science (Graphics, Algorithms, Theory, Systems, Architecture), Mathematics (Information Retrieval, Machine Learning), Engineering (Robotics, Speech, NLP, Image Processing), Physics (Optics), Biology (Neuroscience), and Psychology (Cognitive Science).

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 Algorithms Machine Learning Engineers Need to Know

The 10 Algorithms Machine Learning Engineers Need to Know

It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data.

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.