At a minimum, a data scientist should...

I wish I could remember where I copied and pasted this from, but alas, I cannot. Just the same, here's a great overview of what a data scientist would benefit from understanding.

At a minimum, a data scientist needs to be proficient with concepts such as probability, correlation, variables, distributions, regression, null hypothesis significance tests, confidence intervals, t-test, ANOVA, and chi-square. At an advanced stage, data scientists need concepts and algorithms such as logistic regression, support vector machines (SVMs) and Bayesian methods. Common statistical analysis tools such as Excel, R and SAS are very famous among data scientists.

Python and Matplotlib

Spending Cinco de Mayo learning about the seemingly endless amazing features of Matplotlib in Python!!! I'm currently studying Data Science via DataCamp, which is a comprehensive online education platform that focuses entirely on data science and programming via Python and R. The screenshot below is of a simple histogram that I created by following a series of Python tutorials.

Developing an Understanding of...

I'm creating this post in the spirit of a leaving digital post-it-note for myself. Perhaps in a few months or a year, I will be updating my resume, and maybe I'll feel it useful to list some of the areas of computer science that I have an understanding of. It's quite likely that I will forget details along the way.  Either way, I am currently studying and gaining a working understanding of C, Java, HTML, PHP, CSS, and Python. I am also becoming familiar with Amazon Webservices, Elastic Cloud Computing, Cloud9, Multi-Factor Authentication (MFA), and Git. I'm also navigating streamlined cross-platform source code editors, data science analysis, and software version control protocol.

I'm currently spending as much time as possible; watching videos, working through tutorials, reading articles, and books.