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What is Data Science?

Data science is the world of numbers and statistics, where an individual performs specialized programming using advanced analytics, AI, and even some methods of storytelling to provide insights about the data which is hidden from the business owners. Nevertheless, that’s a very simplified definition of what data science is; in reality, it’s a cross-disciplinary approach, and there are tons of methods for gleaning insights from the ever-increasing data that companies produce every day. The use of data science allows a company to gain insight into all the critical patterns of their business, allowing them to draw conclusions and provide evidence of growth and other essential information to their stakeholders.

Data science isn’t something new, but in recent years, due to the explosion of the internet, more and more data is being produced by customers online, and companies can leverage this information to get insights about how customers interact with their services and what are the changes they need to make to get more downloads from their customers. Today we are going to find why Data science has become an essential part of business and how data insights help a company to reach its financial goals.

Data Science Introduction

Data science, as simple as it may sound, requires a deep knowledge of statistics and mathematical equations. The process involves an individual cleansing, aggregate, and manipulating the data. So it can be ready to be used in a particular type of processing. Data analysis, which is a core part of Data science, requires you to use algorithms, analytics, and even artificial intelligence to accomplish things.

The backbone of data science is the software that actually combs down the data input given by the company and finds out the patterns in it. The responsibility of a data scientist is to develop and implement the algorithms which can be used by software to find the patterns. Also, a data scientist transforms the patterns which he gets from the software into predictions that help in the decision-making for the respective businesses.

After the predictions are made, they are subjected to scientifically designed tests and experiments. The predictions have to pass the tests in order to be validated, or else the data scientist must start all over again. After the prediction has been validated, it will be shared with the stakeholders and the other members of the company via the use of data visualization tools that make it possible for a non-specialist to understand what the prediction is about and how it will affect the company.

As a result, a person who works in the field of data science must be good in computer science and must have pure science skills in addition to specific data analyst skills. A data scientist needs mathematics, statistics, and other scientific methods in order to come out with data and evaluate it using a wide range of tools that requires them to perform tasks like data mining to data integration.

What Are The Different Components In Data Science?

There are four major components for any Data science project. If we think about it, data science is a methodology that involves an individual finding patterns in the given data. With the help of these patterns, a data scientist is able to draw insights and provide the same information to the business officials so they can make informed business decisions that lead to growth and sustainability.

The terms we have mentioned below can be defined in other ways as well, but for the most part, their meaning and the work will remain the same throughout. These four terms are the four pillars of data science, and whenever you are working on a data science project, you need to use these four terms or methodology to derive the best of the patterns and results out from the given data.

1. Data Strategy

The data strategy is basically the part of the process where you gather the data and fix specific rules to choose the right data from a massive pile. As an example, let’s imagine you are a car manufacturer. Some parts of the cars you manufacture and assemble yourself, while others you order from other companies. You will want to figure out if the other manufacturers are charging you more when you purchase a component from them. You need to collect data only for those components that you order, not those you manufacture yourself. Here is a small example of using a data strategy. The data scientist can create a better filter that will reduce the amount of data that needs to be analyzed.

In this step, you are only looking for the data; all the other considerations, such as your business problems, opportunities, etc., will not be part of this step. When you are making a data strategy, you need to form a connection between the data which you are gathering and the business goals which you are trying to reach. Keep this in mind, when looking for data, you must know that not all data is created in the same way. Thus, during the collection of data, you might have to clean the data and format it in a proper way so it can be understandable to the machine. This formatting and cleaning of the data is crucial for determining the value of your business goal, and once you have removed any useless information, the rest will be much shorter and more precise.

When looking for data in your repository, you will indeed find the data, which is good to have for finding the patterns and results. But you have to make a decision and see if the data actually makes any substantial contribution towards your business goal or not.

2. Data Engineering

Data engineering is the step where you will be deciding which technology to use and the systems you need to leverage to access, organize and filter your data. The central concept behind the use of data engineering is to find software solutions that can help you solve your data-related problems. The solutions basically require you to install software that can create data pipelines and endpoints within themselves. But this is no small feat because you will need more than one software or, in some cases, dozens of technologies to churn your data and get the necessary information out from it.

Data engineering is said to be quite crucial for performing data science overall because without using the engineering concepts and software, you won’t be able to perform any scientific tasks on your data. There is no way you can write your own algorithm in order to minimize the image scheduling time. The person who performs the data analysis and codes the algorithms for the same and the person who does the data engineering staff are not the same. Their skillset, however, might look the same, but at the core, they are pretty different from each other. A data engineer needs to have a better understanding of a wide range of technologies and data frameworks. In addition to this, they must know how to combine these together to make a data solution that leads to business processes with the use of data pipelines.

3. Data Analysis & Mathematical Models

Now, this part of the data science implementation can be said as the heart of data science because a lot of things happen in this part of the process. In this section, we take in the data using math or some algorithm, and we try to find out how the system will work. Anything which requires a user to combine computing, math, a specific domain, and an application that will be used as a scientific method will be considered as data analysis and mathematical modeling of data science.

Data analysis can be defined as any method which is used to extract insights or make predictions about a specific service, or a product or even a combination of multiple aspects of an ecosystem.

In essence, a mathematical model is a tool, it will be used as a supplement to a human being, and instead of a robot being put in their place, it will perform the task in the same way a human would.

Both data analysis and mathematical modeling have been present for a long time, but a new change has been brought in by the computing power. Now we have a massive amount of data that needs to be analyzed. Also, previously there were computational limitations as well, so we were not able to perform the mathematical and statistical algorithms which already existed.

4. Visualization & Operationalization

Visualization is more than just taking out the necessary information from the data and presenting it in the correct format. In a lot of cases, a data scientist needs to go back to the raw data and find out what has to be visualized to the stakeholder—at the same time, keeping sure that both the needs and the goals of operations are met. Data complexity is one thing you need to take care of when representing the data; also, make sure you have double-checked all the variables that are used for creating the prediction.

Operationalization means doing some necessary processing with the data in hand. An individual needs to make a decision or take action that is based on mathematics and computing, which happened in the earlier parts of the process. These operations can be completely new, and you will need someone to help you get things clear, or you can use the existing one and get the required result out from your data. One thing we would like to tell you when you are working on a data science project, think of it as creating a product feature project. Because more or less, both of them match the practical point of view.

Why Do Data Scientists Have Higher Packages?

Now that you know the working and the process behind the successful data science project. So let’s move on to the individual who handles such projects. If we look at the annual salaries of data scientists, we will find that even an individual with two years of experience is earning around 7 to 8 lacs annually. This is because the skills required to be a proficient data scientist are pretty hard to master, and it requires in-depth knowledge of both computer science and mathematics.

A middle-level data scientist can easily earn up to 10 lacs per annum in India, and if you look for a job in the international market, it can quickly go beyond 10 lac per annum point.

On the other hand, finding the right candidate for the data scientist job is quite tricky as well, because companies don’t layover and quickly let go of their data scientist, as they are well versed in handling the data for the specific company and can easily spot the patterns in the company’s data which might be difficult for a newcomer to understand in a short time. Thus, data science is hard to learn, and finding a person who has a deep knowledge of data science is much more difficult. As a result, the pay scale is quite significant in comparison to other technologies.

Why Do You Need A Data Scientist In Your Company?

This is, without a doubt, a billion-dollar question, why does your company need a data scientist? Well, first of all, a data scientist will make sense of the humongous data which your company produces and will reduce the horrors which you get to face due to uncertainty in making the right business decisions because of lack of relevant data. Data science is growing at a fast pace, but a lot of industry leaders still consider it to be in infancy. So if you hire a data scientist now, you will still be an early adopter and will be able to outperform your competitor. The role of a data scientist is of paramount importance for organizations across many verticals. So it’s necessary for an IT company to have a data scientist to make sense of their data.


It is estimated that by 2025 only India will be able to have more than 900 million active internet users. In the end, we would like to end this article with one sentence. Data science is one of the most potent and beneficial processes which empowers better business decision-making with the help of interpreting, modeling, and pattern recognition. With a data science expert, your journey of converting your business into a successful and profitable business will become reachable in a short time. Thus, data science is a must-have technology that you need to implement in your business.

Vijay Singh Khatri
Vijay Singh Khatri
Graduate in Computer Science, specialized in Digital Marketing. I am very fond of writing tech articles and creating my own blog to teach my audience.


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