If you’re thinking about getting started writing Data Science content on Medium, don’t get discouraged. What you should realize is that the inherent nature of Data Science writing is different — there’s groundwork that needs to be laid.

Two months ago I left my apartment in DC to move back home for a few months. The idea of saving money on rent, reconnecting with old friends, and working remotely every day was definitely enticing. Since then, I’ve learned about the realities (both good and bad) of working remote as a Data Scientist.

With the onset of Coronavirus (COVID-19), many people are turning to the media to assess the severity of the situation. Some are saying that the media is overreacting, portraying a situation that is far worse than reality. Others believe that the media is strictly portraying the facts, i.e., not trying to persuade us one way or another. How can we determine which is actually happening?

Support Vector Machines (SVMs) are well known in classification problems. The use of SVMs in regression is not as well documented, however. These types of models are known as Support Vector Regression (SVR). In this article, I will walk through the usefulness in SVR compared to other regression models, do a deep-dive into the math behind the algorithm, and provide an example with the Boston Housing Price dataset.

In today’s Data Science hyped world, organizations are increasingly looking for answers from the data they collect. The good news is, this means more business leaders are turning towards data-driven decision making. Unfortunately though, this often turns into you as a Data Analyst / Scientist going on a wild goose chase for answers that may or may not be in the data.