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The emerging data science field is an amalgamation of trending subjects like computer science, statistics, artificial intelligence, and maths. The main objective of this field is to analyze large chunks of data and thereby enhance the growth of the business.  In simple terms, data science enables the analysis of large amounts of data using modern tools and techniques. Read this blog to understand the career prospects of data science.

Key Takeaways:

  • Data science is a new field in demand today because of its effective usage in multiple fields like hospitals, E-commerce, tourism, and finance, it offers an array of job opportunities for data science professionals; hence it is one of the most sought-after careers today.
  • Expertise in AI, computers, Maths, and statistics would be great to start a career in data science since it combines the use of all those subjects.  

What is Data Science?

It is defined as a multidisciplinary academic field combining fields like artificial intelligence, maths, statistics, and computer engineering to efficiently analyze large amounts of data. 

Data Scientist and His Role:


Analytical professionals with expert knowledge in domains like maths, statistics, computers, and AI are called data scientists. This unique combination of skills allows one to efficiently handle complicated issues and analyze the questions that need answers. Since they are highly required in both business and IT sectors, they are well-paid. The everyday activities of data scientists involve the following:

  • In order to get meaningful insights from the data they try to to discover the patterns and trends in data sets.
  • They develop data models and algorithms.
  • With the help of machine learning techniques data scientists enhance the quality of the
  • Providing valuable suggestions to the data science team and other higher authorities.
  • They use tools like Python, R, and SAS to play a vital role in data analysis.

Significance of Data Science:

For an efficient growth of business collecting and analyzing the past data is predominant, and luckily for us data science is a field that allows effective data analysis. To put it simply, data is a revolutionary field that has a positive influence on overall business development. Precisely, it is the backbone of today’s business world. Let us see, how data science has tremendously enhanced modern-day business. 

  • Lately, the newly evolved digital world led to an explosion of data. Be it online transactions, online shopping, or social media interactions, all such digital activities lead to to massive data generation. However, this data is useful only if is appropriately analyzed to extract meaningful insights. Luckily, data science can do this job for us. 
  • Secondly, data science does not necessarily only mean data collection and analysis, it is also about interpreting data accurately so that profound business decisions are made. Through precise data interpretation, companies would be able to anticipate future trends, understand customer’s needs, and thereby follow protocols that increase their operational efficiencies. 
  • Most importantly, the data science field offers profitable job opportunities. The enormous amount of job openings for data science experts is increasing each day; therefore, the salaries are also higher. To sum up, it is one of the most rewarding careers today.

Data Science As a Career:

Data science is a career with longevity and durability because according to the latest sources, the number of opportunities is expected to rise by 28% by the year 2026. The number of job opportunities is expected to shoot up by 28% by the year 2026. Hence, data science is not only high-paying but also highly durable.

What is The Use of Data Science?

From anticipating customer behavior to increasing operational efficiencies, data science has an array of applications. The scope of data science is huge and comprises various types of analytics.


1. Diagnostic analytics:

This step involves understanding the data to know why particular events have occurred. This allows them to understand patterns and identify anomalies. In case of sales depletion in a company, diagnostic analytics allows data scientists to identify the exact cause. Through this step, scientists will be able to identify if there is poor product quality, increased competition, or other factors that led to this.


2. Descriptive Analytics:

It is a form of data analytics that allows analyzing past data to give an idea of events that occurred. The results from descriptive analytics are presented in pie charts, dashboards, and other visually appealing modes for a clear understanding. 

3. Predictive Analytics:

A part of data analytics that uses statistical models to predict future trends based on past data. This is widely used in finance, healthcare, and marketing. For instance, a predictive analytics team in a credit card company predicts customer default risk.

4. Prescriptive Analytics:

This involves providing valuable suggestions based on data analysis to avoid future problems or complications. For instance, in navigating apps, prescriptive analytics shows the fastest route that avoids traffic. 

All four forms of analytics are necessary to extract meaningful data insights that help in decision-making and strategic planning.

Stages involved in the Data Science Process:

Now that you briefly understand “what is data science”, let us quickly dig into the stages that fall into the process.

1. Defining the issue:

The first step involves identifying the issue that needs a solution. As a part of this process, the business goals are clearly stated while understanding important requirements and constraints. Clearly defining the problem helps in setting realistic expectations regarding timelines and goals for a data science project.


2. Collecting and preparing the data:

Data collection is the most critical step of the data science process because having complete quality data directly affects the accuracy of data analysis. Data scientists collect data using internal databases, external APIs, web scrapings, and surveys.

While collecting data, scientists must ensure there is no security breach, and the data scientists  clean, reform, and reconstruct the data, after collecting 

3. Data analysis and exploration:

Post-data collection and preparation, the data is subjected to analysis and exploration. Statistics and data visualization methods are used to gain complete details about the data. 

4. Evaluation:

Based on the data collected in the previous stage, the scientists develop and transform the predictive models. Evaluation parameters like accuracy, precision, recall, and F1 score are employed to evaluate the collected data.

5. Deployment and maintenance:

Bringing the data models into use needs careful planning to reduce disruptions and ensure practical usage. Next, constant maintenance after deployment is also crucial to help sustain model performance. 

Benefits of Data Science:

Data science enhances the growth of businesses, especially the ones that efficiently use data. Effective data-driven practices are necessary to enhance the growth of a business; therefore, data science is crucial in modern-day business. Below are some ways data science is used today.

1. Maximize Business operations:

Data science can greatly improve the growth of business in various departments, starting from logistics to human resources and beyond. Data science plays an important role in resource allocation, performance evaluation, and process automation. For instance, a logistics company uses data science to optimize routes, decrease delivery time, save cost of fuel, and thereby enhance customer satisfaction. 

2. Discover New Insights:

Some patterns and insights may not be noticed at first glance, and here data science comes into play in helping discover missed patterns and insights. Knowing the insights is crucial to providing companies with a competitive edge and understanding the business better. For instance, valuable data insights provide information regarding customers’ needs, thereby companies can tailor products accordingly.

3. Coming up with innovative products and solutions:

Businesses can come up with innovative products and services based on the data related to customer’s needs and preferences. As a result, businesses can compete with their peers and also can stay ahead of them. For instance, online streaming platforms like Netflix come up with personalized recommendations for viewers using data science. 

A Career in Data Science:

Since data science is a complex field, one can begin their journey by taking a data science course. 

Data Science Course:

Famous publication house India Today ranks Henry Harvin’s Data Science course as number 1 in India. Besides regular classroom sessions, you get to attend 12 hours of exclusive sessions from industry experts. Also, for a practical learning experience, they offer more than 19 capstone projects and 38+ case studies. Additionally, they avail their guaranteed internship program in big companies like J.P.Morgan, Accenture, and many such companies.

data science

Their placement support needs an exclusive mention because they offer a 3 in 1 placement support which includes a job portal, personalized job support, and placement drives. As a part of the training, you get to attend 2 mock interviews that would ease you to appear for the actual interview. Hence, Henry Harvin is one of the best ed-tech companies to enroll in a Data Science Course.

Different Applications and Examples of Data Science:

Data science finds its uses in various sectors, below are a few real-world applications using data science that we come across in everyday life.

1. Healthcare: 

Healthcare is one of the prominent sectors, where data science is widely used. For instance, Data science is used to develop sophisticated diagnostic equipment to detect diseases. Also, it is used in drug development that will help cure various diseases. In a nutshell, data science and analytics in the medical field help improve patient care. For example, data science helps develop imaging techniques that can help identify tumors. 

2. Finance:

data science

Since the financial sector is frequently prone to risks and fraud, data science is important in reducing such things. Hence, data science is considered crucial in the financial industry. In the finance industry data science and analytic tools are predominantly used to predict the future. For example, using data science in the stock market allows predicting future stock prices.

3. E-commerce:

data science

Many E-commerce websites like Myntra, Amazon, or Flipkart are able to create a user-friendly interface because of data science technology. For instance, when we search for something in particular, we get suggestions similar to our choices based on our old data.  Also, we get recommendations on the most bought products at the best prices. All this can be done with data science.

4. Airline industry:

The airline industry is growing at a faster pace with the help of data science. For instance, data science helps in predicting flight delays. Also, it helps in deciding whether the flight should directly land at the destination or halt in between is helpful. 

5. Gaming:

Most of the online games we come across have an online opponent, which is usually the computer opponent. Data science along with machine learning is employed to enhance the computer’s performance. For example, online chess, and EA sports use data science concepts.

6. Advertising and Marketing:

In today’s modern world, data science largely finds its use in advertising. More specifically, modern advertising like in airports to display advertisements on websites uses data science models. 

Also, data science has an extremely crucial role in marketing. Typically, when we search for something specific on Google, besides relevant search results, target ads, which are generated by algorithms also pop up. This can be achieved through data science. Precisely, because of data science we tend to see relevant ads to our search results. This is the most important reason why even those belonging to the same region see different ads. 

Most of all, based on the viewership data, businesses analyze customers’ behaviors and customize the advertisements accordingly. As a result, digital advertising is a better choice than traditional advertising.  

Most importantly, famous companies like Netflix, Amazon, Twitter, and many others used data science to analyze customer’s needs and develop products accordingly. 

7. Pharmaceutical sector:

Research and development in the pharmaceutical sector has become a lot easier and more efficient with data science techniques and artificial learning logarithms. Data science provides valuable insights at every step, starting from the beginning phase of medical screening to anticipating success based on biological traits.

Additionally, the algorithms help in generating mathematical modeling and simulations that predict drug responses inside the body. This analysis is extremely useful in developing appropriate biological drugs for medicinal use. 

8. Genomics and Genetics:

The use of data science in Genetics and Genomics allows a high degree of therapy customization. Essentially, data science helps scientists understand the influence of DNA on the drug response of each individual.

9. Transportation:

In the past 20 years, the transport sector witnessed a significant advancement with the help of data science. For example, self-driving vehicles are a classic example of advancement in transportation using data science. Additionally, statistics on fuel usage, analysis of driver performance, and tracking vehicle performance are among other developments by data scientists in the transport sector. 

Prominent, mobile cab booking apps like Uber and Ola use data science to reduce costs. Data science technology in these apps combines customer location, economic data, and logistics for the completion of routes.

Prerequisites to Start a Career in Data Science:

Below are certain technical concepts one must be familiar with before embarking on a data science career.

Machine Learning:

Since machine learning is the backbone of data science, it is essential to a have strong foundation on MI along with statistics.


Statistics being the core of data science, having a firm grip over this subject gains meaningful data insights.


Moedling which is a part of Machine Learning allows for identifying the most suitable logarithm to solve a problem. Knowledge of modeling allows data scientists, data architects, and business analysts to understand the relationship between the data in a database. 


A certain level of programming knowledge is also essential to accomplish data science projects. Especially, Python and R.Python are the most common programming languages that are usually in use.  These programming languages support data science libraries and are also easy to learn. 


A data scientist must be familiar with how databases work and should also know about storing and retrieving data from the database. 

Who Supervises the Data Science Process?

Business Managers:

A business manager typically collaborates with the data science team to analyze the problem and come up with an analytical method. Essentially, they are in charge of the data science training process. Also, the business managers associate with data scientists and IT managers to ensure timely project completion.

IT Managers:

Compared to others, the responsibility of IT managers is extremely important. The main job of an IT manager involves developing infrastructure and architecture to carry out data science activities. Also, they constantly monitor data science teams to ensure safe and effective operations. Additionally, they create and maintain the IT environments, that is crucial for data science teams. 

Data Science Managers:

The managers are responsible for selecting members who are apt for the data science team. Then, they monitor the work carried out by the data science team. In fact, they supervise the everyday activities of the team. In short, data science managers are team builders who are responsible for the planning and efficient execution of projects. 

Career Prospects with Data Science:

Now that you are aware of what is data science and its applications, let us explore different job -roles you can fit into upon completing a data science course.

1. Data Scientist:

As a data scientist, you must have problem-solving skills to identify the trouble. Essentially, a data scientist finds solutions that occur during data collection. Therefore, being in this field requires you to master skills like storytelling and visualization skills along with programming, statistical, and mathematical skills.

2. Data Analyst:

Organizing and analyzing big data is the primary responsibility of a data scientist. He answers and solves the issues that arise within an organization. Essentially, If a data analyst hones exclusive skills like mathematical and statistical skills, besides programming skills, they would find jobs in sectors like finance, healthcare, law, and government.  

3. Data Engineer:

In addition to developing data infrastructure and data pipelines, a data engineer also manages and deploys them appropriately. In other words, an engineer typically builds or creates systems that collect and organize data. Ultimately, their primary goal is to make data accessible to organizations. 

4. Business Intelligence Analyst: 

Mainly, a business intelligence analyst collects, cleans, and analyses data Predominantly, about revenue, sales, and customer engagement information of a business. 

Additionally, a business intelligence analyst also monitors data by programming tools and data. With the data, he comes up with ideas and recommendations that benefit the growth of the company. 

5. Database Administrator:

Essentially, a database administrator maintains and operates databases while ensuring the security and storage of data. Additionally, he assists developers in identifying any issues and implementing new features. In short, a database administrator manages access to the systems that store company information.

6. Data Architect:


They are IT professionals who combine their computer skills with data science skills to review and analyze a company data structure. Also, he designs new databases to store and manage future data for a company and its users. 

Salary Expectations For Data Science Professionals:

The annual average pay for a data science professional is around 14 LPA. Nevertheless, based on factors like location, experience, job role, and company, the salary can range anywhere between 8 – 21 LPA. 


Since data science is one of the fastest evolving subjects, the number of job opportunities that pay high salaries is always higher. Today, data science finds its use in various sectors like medicine, transport, finance, marketing, and a lot more, therefore, the number of job opportunities is countless. Nevertheless, to start your career as a data scientist, honing certain technical skills is mandatory. Taking a data science course is one of the primary ways to start the journey in the data science field. 

Recommended Reads:

  1. What is Data Science and its Career Path?
  2. Top 10 online data science courses with placement
  3. Data science in daily life
  4. 10 Analytical Skills Necessary for a Successful Career in Data Science
  5. Data Science vs Computer Science: Which One is Better


1. Is data science a self-learning program or do we need proper training?

Ans. Data science is a complicated subject that deals with a lot of technical skills; hence it is better to take a proper course instead of self-learning.

2. Must data scientists be aware of coding?

Ans. Yes, data scientists must be familiar with coding because they use programming languages and machine learning to analyze large chunks of data.

3. Where is data science applied?

Ans. Though, there are plenty of fields where data science is useful, Medicine, Fraud detection, Finance, and marketing are among the important ones.

4. To enroll in a data science course are there any pre-requirements?

 Ans. Besides earning 60 % or more in 10 + 2 exams, a minimum knowledge of mathematics, programming, and statistics is useful.

5. Besides a data scientist, what other job profiles are applicable for data science students?

Ans. Besides a data scientist, one can also be eligible for jobs like data analyst or data engineer upon completing the data science course. 

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