The data is everywhere around us. They are usually stored locally or in cloud computing, administered daily by all business departments, combined, and compared to generate value. Data Science is one of the leading partners to make them useful for a company.
The term data science as said by the data science institute in Bangalore, however, seems vague to most people. What exactly does a data scientist do? How is he different from a data analyst? What kind of information can only gather for an organization?
It is these types of questions that we will answer throughout this article. Join us on the journey as we go through the science behind the data
What Is Data Science?
We can define Data Science as constructing structures and solutions for storing, managing, and integrating data. However, as with other terms in technology, this explanation reaches only the surface of the subject.
To understand Data Science, we need to think about the data we currently have. A few years ago, most companies only dealt with structured, low-volume data. Think, for example, of Excel spreadsheets.
Enterprises with BI tools can use this data. But data trends have changed, and we have many other resources that serve to enable more complex information to be included in the generation of business intelligence.
Most of the data we deal with today is not structured or semi-structured. Text and audio files fall into these categories. The sensor information of the Internet of Things also.
To deal with them, we need the help of competent professionals capable of organizing them, creating analysis methodologies for them, and choosing tools compatible with their demands. Hence the recent popularity of Data Science.
Then, we can understand the more complex version of the meaning of these two little words: they describe the combination of many resources, algorithms, technologies (such as Machine Learning and Artificial Intelligence), and skills to explore the patterns inserted in raw data.
The big difference here is that, until then, we dealt with data to explain what was happening within an organization. Now, we use them to predict what will happen.
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