What Is the Scope of Data Science in India?

Data science is the collection, maintenance, and study of data. While modern data science developed over the last few decades, people have been collecting and analysing data to understand the world since civilisation began. Nearly every industry could benefit from data science, so Mordor Intelligence projects the industry could reach $3.38 billion in India by 2029 [1].

When you collect data, you can look for patterns and make predictions based on what’s happened in the past, which can help you run your business more efficiently, save money, and provide personalised experiences for your website or product users. Read on to explore the full scope of data science in India, including industries that benefit from data scientists and careers to consider in the field.

What is the scope of data science?

Simply put, data science is finding meaningful insights in data. Companies and organisations capture a massive amount of data they can use as a tool for business planning. To decipher meaning from raw data, data scientists collect, organise, and prepare data, such as by adding labels or formatting it. Next, they build models to represent different aspects of data and apply those models to predict how events will proceed. 

Data scientists use math, statistical analysis, machine learning, and artificial intelligence to analyse trends and make predictions. As they can work in many different industries and on many other projects, the field is interdisciplinary and marries math, computer science, business, and industry aspects to apply it. Data scientists often collaborate with other professionals to provide technical support to specialised roles. 

The outlook for data science in India

Data science is a massively expanding field in India. According to Mordor Intelligence, the total market size for Big Data in India will reach $2.17 billion in 2024 and grow to $3.38 billion by 2029, growing at a rate of 7.66 percent CAGR [1]. 

When you look at why data science is a growing field in India, you will see that this industry will continue to be relevant for years. First of all, companies can greatly benefit from collecting and understanding data. Technology and software make it increasingly easy for companies to capture data, and they will need skilled professionals to help them know it. 

Beyond the need for skilled professionals worldwide, India is especially positioned to take advantage of the demand for data science. Overall, the Indian data science market earns 60 percent of its revenue from exporting data science expertise to the United States, allowing for expansion as US markets grow [2]. Increased investments and start-up endeavours in the Indian data science space are helping to position the country as a haven for data science skills. 

Industries that use data science

Every industry can collect and use data to identify patterns and make more informed business decisions. Below, you can learn how industries all over India use data science, including financial services, retail, health care, agriculture, and utilities. However, this only represents a selection of the many uses of data science. 

• Financial services: Data science is important in financial services for detecting potential fraud, evaluating how credit-worthy borrowers are, and predicting the performance of stocks or other financial products. 

• Retail: In retail or e-commerce sectors, you can use data to personalise marketing or other experiences to your customers’ needs. Data science can power recommendation engines and create targeted marketing strategies.

• Health care: Data science gives medical researchers and healthcare professionals the tools for faster research, more accurate treatment, and improved patient outcomes. Data science can even help read medical images more accurately using machine learning algorithms and computer vision. 

• Agriculture: In agriculture, you can use data science to build models that use past events to predict weather patterns and crop yields. Farmers can also use data science to understand better what crops grow in what soil or what soil amendments they should make to create a more suitable environment for the crops they want to grow. 

• Utilities: Energy and utility companies can use data science to understand how much energy their customers need or how energy consumption varies according to the weather and other patterns. 

Careers in data science in India

When it comes to the scope of careers within the field of data science, you can find many options to explore with a data science degree, including the more general title of data scientist, as well as machine learning engineer, data architect, statistician, and more. Some of these careers are listed below. 

Data scientist

Average annual India salary: ₹13,65,000 [3]

Education requirements: To become a data scientist, you will need to complete your 10+2 in mathematics, physics, and statistics, as well as earn a bachelor’s degree in data science. 

As a data scientist, you will use statistical models to understand and analyse data, applying your results to real-world problems and finding new or more efficient solutions. In this role, you will help your company strategise and solve problems by collaborating with other professionals to understand how data can drive company decisions and inform business practices. 

Machine learning engineer

Average annual India salary: ₹11,54,050 [4]

Education requirements: To become a machine learning engineer, you may need a bachelor’s degree in computer science, robotics, or a related field. 

As a machine learning engineer, you will use your data science background to design, create, and test machine learning algorithms and software solutions. In this role, you will apply machine learning principles to increase efficiency and accuracy in your company systems. To help your company or organisation overcome problems, you will analyse current systems, experiment to find better solutions, and implement software when needed. 

Data architect

Average annual India salary: ₹28,00,000 [5]

Education requirements: To become a data architect, you will likely need to earn a bachelor’s degree in computer science, information systems, or a related field. 

As a data architect or data administrator, you will be responsible for designing the system within which your company stores and maintains its data. Sometimes, this might require visualisation skills to understand the most appropriate data framework. In addition to building new systems, you may be responsible for designing or creating new data management systems. 

Statistician

Average annual India salary: ₹4,48,000 [6]

Education requirements: To become a statistician in India, you should complete your 10+2 in science or commerce with statistics, then complete a bachelor’s degree in statistics or a related field. 

As a statistician, you may work with a company or organisation, such as a government agency or research and development organisation. You will collect, organise, and interact with a large amount of data to identify trends and make recommendations to shape business decision-making. This role differs from a data scientist role because it focuses on using statistical models to conclude data, while data scientists use a greater selection of tools.

How to get started in data science with Coursera

To work in data science, you will need to focus on maths, science, or statistics, both at the secondary and university levels. Earning a bachelor’s degree is one way to develop a base of knowledge that will help you succeed in the role, but you can find more than one pathway to success in data science. 

Depending on the career title you would like or the industry you wish to enter, you may need to earn a more advanced degree, such as a master’s degree. You can focus on developing your programming and coding skills, understanding data visualisation tools, and machine learning. 

One option to consider is the IBM Data Science Professional Certificate on Coursera. With no prior experience required, this course can help you prepare to start working as a data scientist in as little as five months. It covers topics like importing and cleaning data sets, languages and libraries data scientists use, and more.