We all have a perspective. This is a longer-form version of mine.
This is not an article. Rather it is a more detailed description of my background and credentials as a data scientist, with some opinions sprinkled in. You can think of it as a narrative curriculum vitae.
The basics (organized by time)
- Hometown: Gaithersburg, MD (Montgomery Co., near Washington, DC)
- Virginia Tech, B.S. Chemical Engineering 1992–1997
- …
Yes, there is an abundance of free and low cost resources. However, as you are thinking about Data Science, you should be sure you are asking the right question.
There are an abundance of articles, on numerous platforms, that explain why “You don’t need a master’s degree to get into Data Science!” This is often followed by some kind of a review of the resources available, perhaps the author’s story of how they did it, and lots of encouragement…
A list of common errors students make on graduate school applications (in Data Science, but probably elsewhere as well…)
In case you didn’t know, December is grad school application season. Many application deadlines are in January so hopefully you have already started your applications if you plan on applying. Thus, error #1:
1. Thinking all applications are the same and throwing your application together last minute
This is not the most egregious error, but I see lots and lots obvious cut-and-paste errors. …
From a university faculty member with real-world work experience. Full time school is not for everyone.
I am currently a faculty member at Vanderbilt University’s Owen Graduate School of Business and affiliated faculty at the Data Science Institute. I just wrote about why you should consider a Master’s degree, so now I’m writing the companion piece.
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You’ve seen plenty of student’s takes. Here is what I think as a faculty member in Data Science.
I’ve seen quite a few posts on Medium (and other platforms) giving a perspective on how to gain the necessary skills to be successful in Data Science. The typical contrast is a university degree vs. a bespoke collection of online credentials (via MOOCs, boot camps, etc.). …
Principal Component Analysis (PCA) is a vital tool in the toolbox of the Data Scientist. There are many posts about how to implement PCA and the documentation of how the methods work is often straight forward. What is less obvious is what PCA is doing at an intuitive level. …
