“Data Science” is a modern field that is followed by the profession known as “Data Scientists“. But the biggest issue in most people’s minds is that What is this geek term and What Data Scientists are doing..?
There is a technical definition for the term. The definition is stated as below.
“Data Science is the process of using data to find solutions to predict outcomes for a problem statement“
If you are a techie person, you might understand the above definition. But, for non-techies would require a more elaborate answer for the topic…!
OK, let’s try to read and understand the rest in-detail.
History of Data Science
Around the mid-1990, there was a somewhat popular term called “Data Mining” for process data. The term “Data Mining” was coming into action from an article published in 1996. This article had three authors namely, Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth.
In 2001, William S. Cleveland discovered a new use of data mining by combining it with computer science. He basically used computation power for statistics. This is how the famous term “Data Science” got started.
William S. Cleveland (Image Source: https://www.stat.purdue.edu/people/faculty/wsc.html)
In early 2000, the internet started to raise with revolutionary websites such as Facebook and Youtube. As a result of these kinds of websites, so much data started to generate. However, it was difficult to handle that much data with the existing technologies.
The term “Big Data” was introduced in order to describe such a huge amount of data around 2010. “Data Science” was used to process these massive sets of unstructured data.
Here we are going to take a look at some of the applications of “Data Science” in the real world. Real-world in the sense, your day-to-day life. Does “Data Science” applied in our day-to-day life..? Of cause Yes. When..? Here are some scenarios.
- When you are using Google search engine
- When you are using Youtube app or web
- Also when you are on the Facebook
- And many other popular scenarios…
There should be a definite question in your mind that how the above day-to-day scenarios use “Data Science” in practice. You will find the answer in the next para…!
Each of the massive companies in the above is using the latest technologies to deal with a giant amount of data specially generated by their visitors. Machine Learning(ML) and Artificial Intelligence(AI) are two popular uses of “Data Science” by gigantic tech companies such as Google and Facebook.
So, when you are using one of the above services, ML and AI are playing a big role and that can be considered as few out of many practical applications of “Data Science“.
However, when it comes to the applications, you must remember that, above scenarios are just a few but not limited to.
How It Works..?
The following diagram demonstrates the helicopter look of “Data Science” .
Data Science – Hierarchy of Needs (Image Source: https://hackernoon.com/the-ai-hierarchy-of-needs-18f111fcc007)
The pyramid simplifies the entire need/process of “Data Science” into one single diagram. Also, this pyramid can be used to identify the different job roles related to the big data field.
Generally, the first two layers of the pyramid are the responsibilities of “Data Engineers“. The tasks in the next two layers are done by “Data Analysts“. (You can read further about Data Analyst’s role from the following article. How to Become a Data Analyst). The top two layers are the responsibilities of the role “Data Scientist“.
However, depending on the type of the company and the size of the company, these responsibilities may vary. Some of the bigger companies have the role of “Machine Learning Expert” for the topmost layer of the pyramid. If the company is too small to have more job roles, there can be a one-man show to handle all the responsibilities in the pyramid namely “Data Scientist“.
As you saw, there are no exact responsibilities and exact job roles in the field of big data.
Hope you guys got some idea about our main topic. And share the post with your friends. Stay with us for more posts like this. Cheers…