You heard this term most of the time all over the internet, as well this is the most concerning topic for newbies who want to enter the world of data but don’t know the actual meaning of it.
And for searching the term you landed on multiple blogs, articles as well YouTube videos, because this is a very vast topic, or I, would say a vast Industry.
I’m not saying those are incorrect or wrong even though every article has its mindset behind the term ‘Data Science’.
And in today’s article, I’m sharing my perspective on the term ‘Data Science’, and whatever I have learned till now.
Let’s start with the basic thing if I talk about the formal definition of Data Science so it’s like “Data science encompasses preparing data for analysis, including cleansing, aggregating, and manipulating the data to perform advanced data analysis”, is the definition enough explanation of data science? Well, we can say this is a kind of explanation but data science is more than that.
In my view “It’s a science of getting and mining the insights from the data and those insights will help businesses to grow. And it’s not a technology, it’s a process”.
Just like other processes it also has some tools and technology to make the whole process fruitful, and “It’s not just a Model Building”
Why am I saying this? Let’s understand with an example if we consider web development so there are UI, UX, Database, Networking, and Servers and for implementing all these things we have different-different tools–technologies and frameworks, and when we have done with these things we just called this process as web development.
Just like this in Data Science we have Data Analysis, Business Intelligence, Databases, Machine Learning, Deep Learning, Computer Vision, NLP Models, Data Architecture, Cloud & many things, and the combination of these technologies is called Data Science.
After understanding data science let’s discuss the second concern “Data Science vs AI”. So, we know that data science is a process of getting insights from data and helps the business but where this Artificial Intelligence(AI) lies?
First understand ML and DL so, in Machine learning and Deep learning we perform some mathematical operations on data and make the models, and these models help us to predict future outcomes.
So, it looks like magic but it’s not magic. There is mathematics behind those models and predictions.
If we talk about AI. So, a system that will mimic humans is known as an AI system, and if we look at the ML and DL so these technologies are also doing the same thing, those ML or DL models are capable of predicting future outcomes.
In simple words AI is not a technology, it’s terminology. AI is something where the different-different technology stacks are implemented and built into a system that will be able to make some human intelligence.
So, AI does not lie in the Data Science or outside of Data science, instead, the product of data science refers to an AI solution, but maybe in near future, there will be some technology that will directly work on AI such as humanoid and smart systems without human supervision.
Instead of saying that Data science is changing the world, we can say Data science is helping the world to grow by using data.
Data science will help the business to identify their problems and loopholes as well as give the solution to those problems.
So, data science provides the solution to a business from all directions, and nowadays most businesses use Data Science irrespective of whether the business is small-scale or large-scale business.
If a business produces the data (and we know all businesses produce data) so data science is the right process to crunch that data and get some useful insights from that as per the business use case and problem statement.
Or in other words, we can say that Data Science is the only process that will correctly use the data.
And that’s the reason there is increasing job demand in the Data Science domain.
We know that data science helps to extract insights from data, so how this insight will help the business for understanding this question so, first we have to understand a business use case. (This is a random use case, just for understanding the question)
“ Let’s suppose you have a product-based business where you manufacture and sell the products and there are some products you sold all over the globe like product A, product B, and product C.
Since you have the manufacturing as well as selling units, these units generate some data regarding their work.
There is a Data Science team that is working on the data of both units, and they come up with insights like your Product A is highly demanded in Asia region as compared to the US region whereas your Product B is highly demanded in the US region as compared to Africa region and the Product C is highly demanded in Africa region as compare to US and Asia.
Now you have the insights into the products and for generating more profit you can make a decision like you will increase the manufacturing unit of Product A in Asia or just do targeted marketing of this product only in the Asia region,
and on the other side you will find a problem like why Product B and Product C is not making a profit from Asia Region? and this thing also you will find with help of data science.
Remainder: This is just a random use case as well as random insights.
You will do the same thing with all the products, now you can see how data science is useful for making the business more profitable.
Or in other words, we can say with the help of data science you can identify as well as approach the problem.”
Nowadays most businesses use data science, whether a business is product-based or service-based they use data science for their growth.
There is an Umbrella of Big data and what is Big Data? so, as per its name, Big Data consists enormous amount of data which will be defined by the 4’V Volume, Velocity, Veracity, and Variety.
Where Volume means the amount of Data, Velocity means how frequently data is generated? or we can say the speed of generating the data, and Veracity means how truthful the data is. Variety means the format of data such as text, image, audio, and video.
We produce 2.5 quintillion bytes of data every day, you can get a rough estimate of how much data is generated till now and how much data will be in the future.
And for handling this much amount of data we use such technologies as Big Data and Data Science.
Now, Big Data technologies mostly focus on things like Data Mining, Data Warehousing, Preprocessing Data, and Storing the Data, and Data Science technologies are more towards the Analytical part.
Big Data has the ETL(pipelining), Data engineering, Hadoop, Data Warehousing, and Data Mining whereas Data Science has Mathematics, Machine learning, Deep Learning, Computer Vision, NLP, RL, AIOps, Data Reporting, Dashboarding, and all.
In simple words, Big Data is for handling and managing an enormous amount of data and Data science is for applying mathematics to data so that we can get some insights from it.
Many of my friends ask me the question about “Data Science”, so I thought that through this article I will be able to explain ‘Data Science’ to all those people who do not know anything about Data Science.
So you understood Data Science with the help of this article and I hope that I was able to explain to you the correct definition of Data Science. Whatever I have shared in this article is my experience so far, I am not an expert but a student of data science, I hope you learned something from this article.