Evolution of Data Science
The Internet is a powerhouse of data. Every day we are consumed with lots of data because of the Internet and various devices. The invention of IoT devices has led to more production of data. So, what do we do with all these data? It needs to be mined and processed to get something meaningful out of it. Data about consumer purchase, for example, was used to find out about the shopper’s behavior. Now we have to deal with more volume of data and getting results from them isn’t that straight forward. Statistical models must be used to extract meaning out of them.
Statistical models are the roots of data science. The concept is more than 50 years old, but the first breakthrough of data science came when John W. Tukey, in 1962, talked about the effect of electronic computing on the analysis of data. He had good foresight because the desktop computer wasn’t invented until 1964 and yet he predicted the electronic computing ability.
After IBM released its first computer in 1981, computing started to evolve at a fast pace. People could collect and process data much easier with the help of a computer. During the 2000s, ‘data science’ was first recognized as a separate discipline. ‘Data Science’ as a career choice was recognized in 2005.
In the beginning, data science meant analyzing the data by having a good understanding of the business processes and helping managers with decision making. The type of analysis made didn’t mention anything about the future. Later, as smart technologies and big data emerged, the data scientists not only analyzed the data but also gave insightful predictions about the future using machine learning.
Recently, there has been an exponential growth in computation because of new technologies like virtual reality, augmented reality, the Internet of Things, Nanotech, autonomous vehicles, and others. This changed the role of a data scientist. The data scientist today should have multiple skills to analyze Big data and make necessary predictions. The person should have knowledge of programming, creating data models, perform statistical analysis, understanding of machine learning, and artificial intelligence. So, the data scientist must have the skills of a software programmer, data analyzer, and statistician.
Data scientists are in high demand now. It is a very lucrative career field today. With more digital data and new hardware infrastructure on the way, there will be more need to analyze data in the future. The data science is still evolving. As new technologies are invented, we have to deal with more data. Researchers will come up with more sophisticated data analysis and modeling tools so that the data scientists can analyze the data more precisely and help in major decision making. Data science has given more meaning to data and given us ways to use the data to our advantage. We can now find out ways to solve business problems by analyzing the data we get from various sources.