How the History of Data Science Has Led to the Demand for Data Analysts?
Data science, as a distinct discipline has come about only recently although statistical analytics had existed for thousands of years earlier. Hence, the history of data science is very recent and will have its founding roots in the earlier relevance of statistics, computer science, and arithmetic. Science, accounting, logistics, and human social organization, in general, all depend on statistical data.
The discipline of statistics split off into what is now known as data science when the power of computing changed the capacity to gather, store, and analyse ever-larger volumes of data. Data science as a discipline works on the foundational practices of gathering, preparing, analysing, managing, visualizing, and storing massive data sets. Computer science thus deals with the processing and storage of Big Data.
Brief Overview of the history of Data Science
The primary tool used by Scientists has traditionally been Data for testing Hypotheses. Their interest in data usually relates to their particular objectives and areas of research rather than to data as a separate discipline. John Tukey, a renowned Mathematician is regarded as the grandfather of Data Science whose work is considered a seminal point in the history of Data science. His work The Future of Data Analysis, 1962 is used by many people as the starting point for their account of the history of data science.
Tukey realized how data was brought to life in novel ways by fusing the complexity of statistics with the speed and power of computers. Rather than days or weeks, but in a matter of hours. Over time, hours swiftly turned to minutes, seconds, and eventually nanoseconds. Although originated and was founded on the premises of statistics, however, it's a separate field devoted to comprehending the best methods for gathering, storing, manipulating, and analysing enormous amounts of data.
The data revolution continues to this day. Big data has become the driving force of every action in the complex, globalized and post-industrial civilization. In such a world, there is a rising demand across the economy for qualified data scientists and analysts and a subsequent need for excellent data science course programs and training.
Surging Demand for Data Analysts
Data analysts are in high demand and are in high demand. As of 2019, the average yearly wage for "operations research analysts," as defined by the Bureau of Labor Statistics, was $84,810. Furthermore, the BLS predicts a 26 percent growth rate for the industry through 2028.
This surge in demand for data analysts in turn has increased the value of data science training and data science course programs to shape professionals competent enough to give expert actions.
In a highly competitive modern era, to be a successful data scientist, having both technical and soft skills is critical. The skills are necessary to efficiently evaluate and apply data analytics to particular human needs because data science and its sub-disciplines are present in almost every aspect of modern life. Technical proficiency at the highest level must be adaptable to the work at hand. A data science course serves as the primary step to moving into the data science field and reach for a position at the pinnacle of the field.
