A Beginner's Guide to Data Science: Need, Scope, Focus, and Branches

Data science could seem intimidating, Nirpeksh Kumbhat, Founder & CEO, SkillEnable, explains the basics of Data Science in this article.

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Data Science

Data science could seem intimidating; Nirpeksh Kumbhat, Founder & CEO, SkillEnable, explains the basics of Data Science in this article.


When data scientists talk about Tensor flow usage or about shooting algorithms as a part of their experiment – it just feels alien. To add to the mystery enters – Big Data! It has an aura of modern-day magic, and the scientists are like wizards. You hear data being the new gold, about the urgency to capture, process, and present data. It is too much for the mind to take! To understand thoroughly, it's important to break things down the basics. To the functioning of its practicalities. Let's learn about this field today – as a first step – before deciding whether or not you want to get into data science.

Why do We Need Data Science?

Every functioning business, from the health industry to digital marketing, every organization from the army to education, everyone needs the force of data to keep up with the technological advancements. Technology is the ultimate truth, there is no escaping it. And it is only going to spread its web with every passing day. How people used to operate five or ten years ago is obsolete today. The problems are much more complex, so are the solutions. Every industry and every organization share the same theme. They need to be updated or risk their position in the market riding on the tidal waves.


The way it goes, it is important not just to keep, but it is obligatory to be prepared with a solution to problems that are yet to emerge. Back to the primal question of the segment, why do we need data science! Well –

Data Science for Business

Data is to business what oil is to the Middle East. At every step, companies need data to show them a path by business prediction and forecasting. The available data sets are used to process facts and figures on which the companies base their decisions.


Data Science for Medical Research

Data science has made it quicker and simpler to comprehend the challenges. It has also made it easy for the professionals in the field to get desired results analyzing the data. With data science, scientists today have the power to foresee potential threats, study patterns in patients, and assess the effectiveness of the treatments.

Data Science in Education


Data can help the administrators by showing them a preview of potential forthcoming student batches. Allows them to design innovative programs and courses based on market trends. Data science plays a key role in formulating the grading-system based admission policy.

Can you see a pattern? It's right there! Data is being used in different ways by different industries to stay afoot!

Scope of Data science


There are no bounds to the possibilities with applications of data science. Different sectors implement the use of data science in their ways.

In Automobile Industry

Flying cars, self-driven cars, fixed destination cabs, and many other upgrades in the automobile industry needs data science to flourish. We know it is just a matter of time when these things will take over the world which means the automobile industry is a powerhouse for data science professionals.


In Healthcare

Lately, the healthcare industry has been the most significant player in data science. The patient influx is more than ever, so they are using data science to create datasets that will enable them to detect disease at an early stage.

In Army and Weapons


Countries are making automated solutions to detect potential attacks at an early stage using data science. It is also helping them in making automated weapons intelligent enough to know when and when not to fire.

In Banking and Finance

The hackers are more innovative than ever! With the readily available information, the security is in a vulnerable state. With the incoming of online banking numbers of frauds have spiked, but with the help of data science, the Banking and Finance industry is ready to take on the challenges. Data science will be crucial for safely managing money in the future.

Again, these are just a few examples. The scope is limitless. Every industry needs to work around data to be safe, stay in the run, and prepare for the future.

What does Data Science focus on?

The key role of data science is to make predictions and decisions incorporating prescriptive analytics, machine learning, and predictive causal analytics. But how does it do that?

Machine learning to make predictions:

Assuming a financial company gives you its transactional data and asks you to create a model for determining the upcoming trends – you will use Machine learning for making predictions. This comes under the umbrella of supervised learning. It is termed that because the data is already there and all you need to do is train the machines based on it.

Predictive causal analytics:

If you need a model that has the capability to predict all the probabilities of a certain upcoming event, you will have to apply predictive causal analytics.

Assume that you give out money on credit. It will be highly crucial for you that your consumer makes her payment on time. Using this, you can create a model that will tell you the payment history of the consumers so you can predict whether the payments will be timely or not.

Machine learning for pattern discovery:

Sometimes companies do not have parameters on which they can base their predictions. In such situations, you will need to look for patterns to make meaningful predictions within the dataset itself. Clustering is a common algorithm used for such discovery of patterns.

Prescriptive analytics:

If you need to have an intelligent model with thinking of its own, the ability to make decisions, and adjust to the dynamic parameters – Prescriptive analytics is your answer.

The self-driving car of Google is an apt example for it. Data is gathered by automobiles which are then used to train the cars so that they can drive themselves. The car will know when to stop, when to go fast, and when to turn.

Data Science Specialization

Now, let's talk about the opportunities to build on your strength and have a successful career in this field. Now you know that there are abundant opportunities in the field. It is blowing up, and it is the best time to throw yourself in the tidal waves of the data science industry. At the same time, you must play on your strength, it is important. Discover which roles challenge you, and which roles you are good at. Make a well-educated choice. Dive in:

Data Mining and Statistical Analysis

In this role, candidates detect and discover useful structures of data and map all of it to create resourceful info. It helps the data science experts to deliver exploratory data analysis with predictive models to extract trends and patterns in the data.

Jobs: Data analysts, Statistician, Business Analyst

Business Intelligence & Strategy-Making

This data science domain specializes to recognize the areas where an organization needs to strengthen, at the same time, determine their acute areas of revenue loss. This is made possible with the help of intricate mining of datasets implementing Business Intelligence software. To get the desired result, they also analyze competitors and the trends they are following.

Job: Data Strategist, BI Engineer, BI Developer, BI Analyst

Data Engineering and Data Warehousing

Data is not understandable to the layman unless it is presented in a meaningful manner. This is what this specialization is all about—turning data into a useful resource by presenting it in an understandable format.

Jobs: Database professionals, Data Analyst, Data Engineers

Data visualization

Data visualization is a data science specialization domain that is responsible for representing data and information into a graphical demonstration. This specialization deals with data to create a graphical demonstration. Data visualizers need to use the tools like tables, graphs, plots, maps, as well as infographics. Using these tools they create a streamlined way to keep an eye on competitors and understand trends, data patterns, growth rates, and much more.

Jobs: Software Developer, Data Visualization Engineer, Data Scientist

Database Management and Data Architecture

This is the era of big data and data science. Centralized data architecture aligned with the standards of the industry is crucial for organizations. Once the data is cleaned, it becomes ready to be maintained and deployed in the company database which will be used in the future for various processes.

Jobs: Database Administrator, Data Specialist, Database professionals

There are other specializations like:

• Machine Learning and Cognitive Specialist

• Market Data Analytics

• Cybersecurity Data Analysis

• Operations-Related Data Analytics

We hope that insight into data science helped you deepen your understanding and helped you to know the field a bit better than you did 10 minutes ago.

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