Individuals are relinquishing, consuming, and producing data more than ever. Photos, videos, mobile calls and personal note all contribute to the growing amount of information that is being exchanged and stored everywhere. This information, if properly analyzed, can be used to understand human behavior both on a personal and collective level. Data scientists take advantage of such publicly available information in order to trace and better understand trends in the data.

The value of data comes in its interdisciplinarity. Scholars from different backgrounds have either learned or developed the tools to analyze the information and come to predictive conclusions. Data can be passively accumulated from different sources such as mobile applications, websites, search engines, etc. The efficient and effective usage of this data could help experts enhance our prediction of the future by understanding the present in a way that is only recently getting traction.

For instance, one simple application of data science that has remarkably simplified our lives is the building of recommendation systems. A recommendation system tries to guess where your interests lie by analyzing the history of your activity. Applications and social networks like Facebook, Twitter, and Google have notoriously used these systems to target consumers with specific ads, people, and events tailored to their individual tastes.

Behind the Machine: Data Scientists

Data scientists rely on statistical models and machine learning algorithms to learn from data. And although the term may sound intimidating to some, machine learning helps scientists understand the algorithms being fed into the machine in order to predict their outcome. A crucial part of machine learning is, after all, learning. Data scientists teach the machine to make predictions about the future using data based on a model or an algorithm. So, it’s important to keep in mind that the computer is just learning what we are teaching it. In this complicated process, human intervention is important in order to evaluate the model’s performance, success, and failure in prediction. By intervening, data scientists adjust the parameters and factors that make up their model in order to minimize any bias or error the model is prompt to make.

Learning from data, humans and machines have been able to draw tangible knowledge and concrete meaning from information through the effective interpretation of that information. No wonder data science is a fast-growing field. According to an article published by CNBC, data science as an occupation was ranked first in the United States in 2017. The ranking classified different occupations based on three criteria: the average annual salary, job satisfaction, and the number of job openings. Data science jobs are expected to gain center stage in Lebanon and other countries around the region as the digital economy grows.