Data science is a broad career path and is undergoing developments and thus promises abundant opportunities in the future.
“The ability to take data — to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it — that’s going to be a hugely important skill in the next decades.” - Hal Varian, chief economist at Google and UC Berkeley professor of information sciences, business, and economics
As the world is era of big data, the need for its storage also grew. It was the main challenge and concern for the enterprise industries until 2010. The main focus was on building framework and solutions to store data. All the ideas which you see in Hollywood movies can actually turn into reality by Data Science. Data Science is the future of Artificial Intelligence. Data Science is one of the top trending technology of the era and data science has stretched its roots deep down in the corporate industry. Due to this vast inference, new age learners and working professionals are keen to learn this technology. To curb this, various online data science training are available through which one can master data science and begin career as a data scientist.
Therefore, it is very important to understand what Data Science is?
In this article, I will be covering the following topics-
- The need for Data Science.
- What is Data Science?
- What is Data Scientist?
By the end of this blog, you will be able to understand what Data Science is and its role in extracting insights from the complex sets of data.
Let’s Understand Why We Need Data Science?
Earlier, the data that we had was mostly structured and small in size, which could be analysed by using the simple tools. Unlike data in the traditional systems which was mostly structured, today most of the data is unstructured or semi-structured.
This is not the only reason why Data Science has become so in trending. Let’s dig deeper and see how Data Science is being used in various domains:-
- How about if you could understand the precise requirements of your customers from the existing data like the customer’s past browsing history, purchase history, age and income. No doubt you had all this data earlier too, but now with the vast amount and variety of data.
- Let’s take a different scenario to understand the role of Data Science in decision making. How about if your car had the intelligence to drive you home? The self-driving cars collect live data from sensors, including radars, cameras and lasers to create a map of its surroundings. Based on this data, it takes decisions like when to speed up, when to speed down, when to overtake, where to take a turn etc.
- Let’s see how Data Science can be used in having the effect of predicting an event or result. Let’s take weather forecasting as an example. Data from ships, aircrafts, radars, satellites can be collected and analysed to build models. These models will not only forecast the weather but also help in predicting the occurrence of any natural calamities. It will help you to take appropriate measures beforehand and save many precious lives.
What is Data Science?
This aspect of data science is all about uncovering findings from data. Granular data is detailed data, or the lowest level that data can be in a target set level to understand complex behaviours, trends, and inferences. It's about surfacing hidden insight that can help enable companies to make smarter business decisions. For example:
- Netflix data mines movie viewing patterns to understand what drives user interest, and uses that to make decisions on which Netflix original series to produce.
- Target identifies what are major customer segments within its base and the unique shopping behaviours, which helps to guide messaging to different market audiences.
What is Data Scientist?