Data Science is one of the technical terms presently, but what exactly it is before we go on to learn it evolved from its primitive nature.
Introducing Data Science
It is a science of mixture of various tools and models, including statistics, analytical tools, machine learning algorithms, and Artificial Intelligence models. So, how exactly is it different from what statisticians did earlier, you ask? The statisticians used statistical models to analyze the data available to explain why things went the way they did.
But that was it. The administration of the companies had to act upon the recommendations to make changes. This would be sometimes counterproductive because, by the time statisticians compiled and analyzed the data manually, the dynamics of the business environment changed over time. However, that also didn’t mean that they stop doing the analysis altogether. Therefore, a need for quicker and prediction-based tools arose. With the advent and advancements in the information technology, programmers were now able to work with statisticians to apply their models in their algorithms and through machine learning and Artificial Intelligence, they saw a pattern that could not provide analysis, but also predict the future outcomes to an extent.
From statistics to computer programming
Like mentioned earlier here, statisticians used to compile available data to churn recommendations or suggestions manually. It all started with statistics. But with IT, more data was ever being captured. This massive data needed a machine that is capable of processing and delivering reports in a snap. Therefore, primitive data processing models were built by software developers in cooperation with statisticians. Later on, technologies like Machine Learning and Artificial Intelligence took off and provided fairly good outputs that could be used by businesses to improve their sales, reduce redundancies, cut down costs, eliminate unnecessary resources, and streamline their whole end-to-end operations better.
Data scientist today knows both statistics and computer programming to build efficient data, science models. However, when it started, statisticians like John Turkey opined way back in 1962 that statistics should be merged with computers. Additionally, Peter Naur also authored and published a paper in 1974, where the term “Data Science” was used several times.
The advent of cloud paved the way
The computer models that incorporated statistics could only process a limited amount because the processing power of a standalone computer system doesn’t suffice. Thus, it needed the combined processing power of several powerful machines. Voila! In 2001, the cloud-based “Software as a Service” was developed that paved the road for humongous processing remotely. From then, companies developed and multiplied the efficiency by building several efficient data science models of their own. In 2013, IBM shared statistics that said 90% of the world’s data was captured in the last two years. The data explosion required much more processing and computation power. Subsequently, more than many other companies put their efforts into data science R&D, which has now made Data Science truly global. All the companies around the world are now relying on data prediction to strategize and emulate various business models.