Data science is an intricate and important part of what we do at Geneia. By identifying certain patterns in data, we are able to optimize care and improve efficiency and patient health. Analytics and data science are essential for healthcare improvement and from my vantage point as a college student, it is exciting to see the field reshape each day. At Geneia, we use data science solely to improve healthcare; however, I also am fascinated by the lighter side of analytics.
If you are active on social media, it’s likely data science is used every day to personalize your experience. YouTube and Facebook are great examples. If you watch YouTube videos, you’ve likely noticed the section titled ‘recommended for you.’ YouTube is able to do this for each individual user by using an algorithm that incorporates user activity and information from videos already watched. Similarly, Facebook displays user-specific advertisements based on geographic location, personal interests and activity.
Whether you realize it or not, data science is used virtually everywhere. Here are a few more interesting and fun examples of how data science is used:
Although I am not a consumer, I understand it is hard to find the perfect wine. Unless it’s your personal preference, no one wants a wine that is too dry or too sweet. There are many factors that play into finding the perfect wine, and for some, it can take years to find a favorite.
A company called Bright Cellars aims to simplify wine choice. They have created an algorithm that is able to give personalized wine matches based on answers to a seven-question quiz. The company uses your answers and its algorithm to recommend four wines. You’ll have to take the quiz to judge the accuracy of their algorithm.
Along with wine recommendations, data science can also help predict the outcome for the NCAA Men’s Division I Basketball Tournament, also known as March Madness. Before the first day, fans everywhere complete brackets, predicting the winner of each game.
Anecdotal evidence suggests most predictions are based on team statistics and people’s gut instinct. It isn’t too tricky to determine which NCAA team is better than another based on team statistics; the hard part of March Madness is knowing whether there will be an upset.
That’s why people began creating algorithms that analyzed past data to calculate the chances of an upset. One algorithm designed by two math Ph.D. candidates at Ohio State University had a 75 percent success rate when it came to predicting upsets in the tournament. If you’re betting money on the tournament, you may be better off relying on an algorithm instead your gut.
Game of Thrones
The show Game of Thrones is popular and has received many awards for its cinematography, casting and sound effects. For those who do not watch the show, it is about two powerful medieval families fighting for control of the Seven Kingdoms of Westeros as well as to sit atop the Iron Throne. Viewers are engaged in the show and sometimes get angry at how often characters are killed.
If you are viewer who doesn’t like surprises, it is possible to predict the odds of a character’s death by applying data science. Using the book that’s the basis for the show Song of Ice and Fire and a fan web page, a man named Taylor Larkin created algorithms that enabled him to calculate the chance of death for all the main characters in the final season. Since the final season has not been released yet, we will have to wait to see how accurate his algorithm is.
These examples are just a few of the many data science applications that touch our lives every day. Their predictive capabilities are amazing – spanning from the mundane to the practical and life-saving. Whether you want wine recommendations, a leg up on your March Madness bracket or to find a way to improve patient health, data science can and is increasingly used to help.