What is Data Science?

As the world progressed towards technology and digitalization, it was giving rise to humongous amounts of data. Initially, the challenge was to store the data. But when the storage started to become cheap and feasible, the focus shifted to processing this data. That marked the beginning of what we today call Data Science.

Let's understand Data Science by first understanding Data.

What is Data?

Usually, when we hear the term “Data”, we think of numbers, tables, charts, etc. But there’s a much broader spectrum of recorded things that can be classified as Data. For instance, the GDP of a country, annual sales of a company, stock market records, movies on Netflix, tweets, bills, birth records, medical records, etc are all examples of data. Even reading this article is being recorded as an instance of data at some digital storage.

You can now imagine the amount of data that is being produced every minute (as we speak). Or maybe you can’t. Here’s why:

• Almost 150,000 emails are sent every minute
• 3.3 million Facebook posts are created every minute.
• 3.8 million Google searches are performed per minute.
• Almost 66,000 photos are uploaded on Instagram per minute.
• 44,900 Tweets are constructed every minute.
• 500 hours of YouTube videos are uploaded every minute.

These are just a minute’s details of some of the popular sources of data. The actual amount of data in the world is quite overwhelming. By the way, it’s many Zettabytes.

Now that you have an idea of what Data is, let’s understand how this data is made useful with the help of Data Science.

What is Data Science?

Suppose there is a pandemic and various health organizations want to know if the disease is more likely to kill certain kind(s) of people than others. To check that, they must have the records (data) of all the victims. Once they have the required data, they can use various tools and methods to figure out a trend or a pattern among the victims that died. This will enable the organization to make certain decisions like spreading awareness or devising a suitable vaccine. Here, the process behind using the data to draw some useful insights and then taking the necessary actions are the applications of Data Science.

Data Science is a blend of extracting knowledge and insights from raw data (Descriptive Analysis), predicting the outcomes based on historic data (Predictive Analysis), and suggesting actionable measures to make business decisions (Prescriptive Analysis).

To achieve the above goals of Data Science, many tools and algorithms have been developed over time and are being used by Data Scientists and even Statisticians in various domains.