How Predictive Analytics Energizes Data Science
The practice of data science has been a part of the human experience since they started to figure things out. What was once a product of experience has blossomed to become a major factor in business, sports, and security for nearly every part of the world. The principal force behind this evolution of data storage and use is the computer.
Today, businesses, governments, and individuals store an immense amount of data on servers. The access and manipulation of data streams have birthed a new field of human endeavor—Data Science and its driving force Predictive Analytics. The existence of these new terms begs the question, “What is predictive analytics in data science?”
The New Field of Data Science
There have been many revolutions in human history. The weaving revolution that moved humans from wearing animal skins to woven materials is a significant event in human history; as is the agricultural revolution that moved social norms from migration to established civilizations. The current revolution is the revolution of information gathering called the Information Age. After the invention of the computer, data gathering took off. Some businesses used data to find new markets; others simply gathered the information to make contacts with prospective customers or to sell to other businesses.
As explained by Forbes, ”Data Science” first appeared in the 1980s and became a field of interest for businesses. In the first decade of the 21st Century, schools answered the call for data science expertise by creating degrees that concentrated on the gathering and use of data. Today, data science has grown to be considered the sexiest of fields in which to work. Data science is no longer just the accumulation of facts and historical norms; the area is energized to predict future events, attitudes, and economic factors. The change in the field is due to the use of predictive analytics.
While the ability to accumulate data is what elevates humans, the use of the data elevates humans above animal status. However, the accumulation of data until the 1970s was based on personal experience. Today, people have access to immense amounts of data on computer servers. By digesting that material, people can come to an informed decision. What Predictive Analytics has accomplished is to push those decisions into action.
Forbes shares how predictive analytics involves the analysis of historical data for setting a sense of direction toward the solution of a given question. With the use of Predictive Analytics, stockholders can estimate the value of stocks a week, month, or year in advance; businesses can predict which customers will purchase which products; governments can predict the effects of trade agreements far into the future.
Predictive Analytics in Data Science
When asking the question, “What is predictive analytics in data science?” we realize that the original use of the “new-fangled” computer to gather data about customer preferences was an attempt to utilize a new science to enhance business decisions. However, the accumulation of data is not enough. There is a need to align the data toward a given purpose—to decide on new products and new directions that may affect the business’s presence in the market. Predictive Analytics is the spur that generates informed decisions. The task of sifting through data to find a market tendency is a monumental task. Predictive analytics produces computer models to predict future markets in every form. Without predictive analytics, data science would be a frustrating practice.