It is an interesting time to be a data scientist. Tools, techniques, and expectations in different sub-genres are evolving asymmetrically. Most notably, I feel that visualization has lagged machine learning for quite a long time. I must clarify: While colorful corporation-friendly visualization platforms are thriving, technical visualization tools used by data scientists remain stagnate. By […]
Programming Options for Rapid Prototyping
Nowadays, programmers typically know more than one language. In this post, we will make a strict delineation between scripting languages and programming languages. Scripting languages are designed from the ground up for specific purposes and applications. Programming languages, on the other hand, are designed for broad applicability. Experience with multiple scripting languages is better than […]
Documenting Codebases for Teams
A properly prepared team can make or break an expensive development effort. When I prepare a team to work together on an application, I like to keep four distinct documents in the source code that detail how the application works, how it is programmed, how to use it, and what we are working on. In […]
Why PhD’s are Great Hires as Programmers
In 2017, there are a lot of programmers in the world. The talent pool is enormous. As languages evolve and technical education becomes more accessible, a growing number of professionals are either self-taught or converted from another area of expertise. At the same time, computer science curricula in universities are evolving to focus on more […]
Machine Learning: Visualizing N-Dimensional Data
Data scientists are often faced with the task of understanding and modelling multivariate data sets. As any experienced data scientist will tell you, it is usually hard to wrap your head around data with more than 4 columns. While we can plot relationships between each variable individually, it is difficult to intuitively understand multivariate patterns. […]