3 Effortless Tactics to Be a Data Science Success in Business
“Move out of the way – I am ready to model.” That is the typical sentiment of a Data Science team when given a business problem. However, in the context o...
Category: Thoughts
A Discriminative Feature Space for Detecting and Recognizing Pathologies of the Vertebral Column
ABSTRACT: Each year it has become more and more difficult for healthcare providers to determine if a patient has a pathology related to the vertebral column. Th...
Creating Value for Business: 2 Data Science Questions You Must Ask from the Start
Business goals are no doubt important, but in an analytic project it makes sense to balance the organization’s goals with those of the Data Science depart...
How Is Knowing the Business Important to Data Science?
Businesses around the world are involved in a multitude of projects at any given time. As Data Scientists come into the business fold, it becomes more important...
7 Questions Every Data Scientist Should Be Answering for Businesses
Business professionals of all levels have asked me over the years what it is that they should know that their Data Science departments may not be telling...
Introduction to Inference and Learning
Many of my subscribers have asked for some resources to help get them on a path for better understanding with regards to inference and learning. As many individ...
Getting Started with Machine Learning
In truth, I am an advocate for jumping in head first and using what you learn in real-time. Practically speaking this means learn less about all the theory and ...
How to Become a Data Scientist
How does one become a data scientist? Well, in truth, the path is most certainly clear. However, the work it takes to travel down the road is not for everyone. ...
Seizure Detection in EEG Time Series
I had a wonderful opportunity to work Eben Olson of Yale University on a problem data set provided by The Mayo Clinic. He and I did a write-up on the what we le...
Do I Have to be Great at Math?
In many cases, individuals try to start out knowing all the math behind the data science before being practical with the science. The majority stop here before ...