How to become a Data Scientist in India

Who is a Data Scientist?

The role of a data scientist is to collect, analyse, and interpret large amounts of data and provide statistical insights. 

The  Data science world contains different jobs that include:

• Data Scientist: A normal day for a data scientist includes finding patterns and trends in big datasets with the help of programming languages like Python, SQL, R, and SAS. Data Scientists require more experience and are considered more senior than data analysts.
   
• Data Analyst: Data Analyst are responsible for analysing and organising data and creating reports related to the organisation's sales, marketing, logistics, etc., using tools like Python, R, SQL, Tableau, or Power BI.
   
• Machine Learning Scientist: This role focuses more on the algorithms part rather than focusing on model-building. They work alongside data scientists to build products from the models. 
   
• Business Analyst: Use the data provided to assess and form an insight to improve the efficiency in the workflow using tools like SQL, Excel, Power BI, and Tableau.
   

Data scientists are making a meaningful impact on the software industry by using data to improve software products' quality, usability, and performance. They do this by enhancing user experience and optimising software performance. They use data analytics to understand user behaviour and preferences and optimise user interfaces and experiences. 

They use data from system logs, application metrics, and other sources to identify performance blockages and optimise system performance.

How to Become a Data Scientist in India?

The first and foremost step is to get a relevant Bachelor's degree. These may include fields of engineering, mathematics, statistics, economics, and finance. The following is the rundown of other crucial steps to follow on the road to becoming a Data Scientist:

• Education Requirements: To be a data scientist, you should have a bachelor's degree. Having a degree in Computer science, Mathematics, and Statistics is preferable. Having a Masters's degree or PhD is a plus point.
   
• Learn how to code: Typically used programming languages are Python, R, SQL, and SAS. In hierarchical terms, Python stands at the top while others follow along. These are essential while working with big data.
   
• Work on statistics: The role of statistics is very crucial when it comes to mathematical modelling and machine learning algorithms. It helps you to understand the nature of data. How is it behaving, what the trends are and much more? So, brush up on your calculus, algorithms, and statistical skills.
   
• Get certifications: Getting a certificate not only helps you to get a headstart on your career but also helps to build a good resume as well. For a Data Scientist job, get certified in the previously mentioned programming languages and skills to up your game.
   
• Get internships: "Once you have developed your skills, apply for internships to familiarise yourself with professional settings. Securing an internship provides you with relevant experience in this field. Preparing for interviews will also help you gain confidence and condition the process of giving an interview. 
  
  You will grow and nurture under the guidance of your mentors and face the real corporate world. Once you have completed your internship and a data science course, you can start looking for entry-level jobs like data scientists, data analysts, and data engineers to go up the ladder as you expand your set of skills and knowledge.

Skills Required to Become a Data Scientist

The following are some technical skills that a data scientist must be proficient in:

• Statistics: Statistics is essential to data science because it provides proper data analysis methods. Making the huge data useful for us, Observing the data behaviour, and making conclusions from the data. Statistics are also useful when creating data models for different use cases.
   
• Programming: It enables the manipulation of large and complex datasets efficiently by using indexing with the help of programming languages like Python, R, and SQL. Programming also allows data scientists to automate repetitive tasks by using looping and conditional constructs. A solid grasp of programming concepts and proficiency in one or more programming languages is necessary for a career in data science.
   
• Mathematics: Machine learning algorithms, performing analyses, and discovering insights from data require mathematics. The use of mathematics heavily influences models.
   
• Data Visualisation: Viewing data in a visual form would be appealing compared to seeing it in a tabular format. As the name suggests, visualisation does the same by interpreting data into a more accessible form that highlights trends from the data.
   
• Big Data: Big data is data having high volume, velocity and veracity. The data that data scientists use daily is quite huge, and normal algorithms can’t be applied to them. So for a Data scientist, it is crucial to understand how to work with such data.
   
• Machine Learning:  Machine learning, in simpler terms, is making machines learn from data and giving trying to give intelligence equivalent to humans. Machine learning is essential in automating processes by observing past data trends.
   
• Data Analysis: This is a key component of data science. Many analytics tools and technologies are available; mastering them is required in any data science job. Proficiency in devices such as Microsoft Excel or Google Sheets, Business Intelligence tools like Tableau and Power BI are in demand. 
  
  Power BI and Tableau are data visualisation tools allowing users to create interactive dashboards, charts, and reports from various data sources. Power BI is a tool by Microsoft, while Tableau is a standalone data visualisation software. 
   

Along with these technical skills, these are some soft skills that will make your career as a data scientist shine.

• Communication: day-to-day work heavily relies upon communication to convey recommendations and findings to staff from non-technical branches.
   
• Problem-solving is used to formulate hypotheses about the problem and potential solutions.
   
• Critical thinking: is used to infer facts without any discrimination, study the context, solve challenges, and come up with applicable answers
   
• Attention to detail: is a vital skill for any budding data scientist. It helps to identify initially neglected relations and details.
   
• Collaboration and teamwork: Data scientists should be able to work collaboratively in teams and communicate effectively with departments and functions. They should be able to work with subject matter experts and analysts to understand needs and translate data.
   
• Continuous learning: Data science is a rapidly evolving field, and data scientists should be committed to persistent learning and staying up-to-date with the latest tools, technologies, and trends.

Data Science in India

As of now, India has been going through a boom in the IT industry.

Data science is accepted in various sectors and industries in India, including healthcare, finance, e-commerce, manufacturing, and education. With the growing demand for data-driven models and the availability of advanced analytics tools, businesses and organisations in India rely on data science to improve their processes and gain an advantage.

Furthermore, the Indian startup ecosystem is growing, and many startups are leveraging data science to drive innovation and growth, creating new job opportunities for data scientists.

Here are some industries where data science is used:

1. Healthcare: to analyse patient data and develop personalised treatment plans. It has also been used to predict disease outbreaks, identifies health risks in specific populations, and improve disease administration.
    
2. Agriculture: to optimise crop yields and improve agricultural productivity. by monitoring soil moisture and predicting weather patterns.
    
3. Education:  to develop personalised learning plans for students looking for a specific career and identify areas for improvement in the education system.
    
4. Smart cities: to develop models which can be used to optimise traffic flow, monitor air quality, and reduce energy consumption.
    

Overall, the scope of data science in India is immense, and it is an excellent career option for those interested in analytics, statistics, and programming.

Future of Data Science in India

The World Economic Forum states, "It is estimated that 463 exabytes of data will be created each day by 2025, globally".

The above statement suggests that the global demand for data scientists will increase by 200% by 2026. The future of data science in India looks bright and promising. India is emerging as a data science and analytics hub. Analysts predict that during the next five years, India will have more than 11 million job opportunities in data science.

For instance, during the onset of the COVID-19 pandemic, data science was used to forecast the spread of the virus, tracing apps and systems, vaccine distribution and treatment optimization. Further, during and after the Covid-19 pandemic, the surges in online marketplaces, online education, and e-commerce platforms generated large amounts of data. 

This influx provided the Indian job market with a new array of jobs required to harness these massive chunks of data. All of this combined boosted the extent of Data Science in India.

Top companies hiring Data Scientists in India

We discussed how to become a data scientist in India. Now let's discuss the top companies hiring data scientists in India.

• Intuit India: Intuit is a software company focused on the finance of personal and small businesses. Intuit is a leading provider of financial management and accounting software. Intuit India is responsible for developing several popular products, including QuickBooks, TurboTax, and Mint. Intuit India deals with large volumes of data daily. 
  This data can be related to customer transactions, financial records, user behaviour, and market trends. 
   
• American Express: American Express is a global financial services company that provides millions of customers worldwide with credit cards, payment solutions, and other financial services. American Express has a significant presence in India, with operations in several cities, including Gurgaon, Bangalore, and Mumbai. 
  
  Data scientists are essential to American Express's success in providing innovative, data-driven solutions that improve customer experiences and reduce risk using fraud detection models.
   
• Amazon: Amazon is the biggest e-commerce platform in the world, operating across five continents with around 20 countries, including India. In India, Amazon offers customers a wide range of products and services, with their priority being speed, convenience, and value. Data scientists are crucial at Amazon India to drive business decisions and improve customer experience. 
  
  Amazon India's dynamic pricing model deals with numerous daily financial transactions. Data scientists are needed to develop fraud detection models to detect and prevent fraud on the platform and to create pricing models that optimise pricing for different products and segments.
   
• Uber: Uber collects vast amounts of data from its app, including information on the rider, driver, traffic patterns, and ride duration. By analysing this data using machine learning models, Uber can identify areas for advancement and adjust its services to improve efficiency, security, and customer satisfaction.
   
• IBM: IBM is a global technology company that has been operating in India for over 25 years and is a top provider of technology solutions and services. In addition, IBM offers services like research and development, education and training, and industry solutions. 
  
  At IBM, data scientists are needed to uncover insights hidden in an organisation's data to guide decision-making and strategic planning.
   
• Walmart Global Tech India: Walmart Global Tech is the technology arm of Walmart, one of the world's largest retailers. The data science team at Walmart Global Tech focuses on using the latest research in machine learning, statistics, and optimization to solve business problems in pricing, sourcing, customer, and supply chain within Walmart's global operations.
  
  Data scientists mine data, show insights, extract information, build analytical models, and deploy Machine Learning algorithms.
   
• Swiggy: Swiggy's success in the highly competitive food delivery industry is primarily driven by its ability to provide superior customer experience and optimise its operations by perfecting personalised recommendations of restaurants, dishes, and offers based on the order history. Further, Swiggy uses data science to predict delivery time based on various factors such as distance, traffic, and weather conditions. 

Data Science Jobs Based on Location

Data scientist jobs are available across various locations in India. Some of the popular cities for data scientist jobs in India are:

• Bangalore: Bangalore is also known as the Silicon Valley of India. It is one of the most popular cities for data scientist jobs in India. The city is home to several tech companies, startups, and research organisations, providing ample opportunities for data scientists.
   
• Delhi-NCR: The Delhi-NCR region, including cities like New Delhi, Gurgaon, and Noida, has a thriving startup ecosystem and hub for e-commerce, finance, and healthcare companies, creating many job opportunities for data scientists.
   
• Mumbai: Mumbai is a hub for the financial industry in India and is home to several banking and financial services companies. It is also home to several media and entertainment companies, providing ample opportunities for data scientists.
   
• Hyderabad: Hyderabad is emerging as a hub for technology startups and research organisations, providing many opportunities for data scientists.
   
• Chennai: Chennai has a growing tech industry and is home to several software companies, providing opportunities for data scientists.
   

Apart from these cities, data scientist jobs are also available in other cities like Pune, Kolkata, and Ahmedabad. Many companies also offer remote work options, allowing you to work from anywhere in India.

Where to find Data Science Jobs in India

There are several job portals and websites where you can find data science jobs in India. Some of the popular job portals are:

• Naukri.com
   
• LinkedIn
   
• Indeed
   
• Glassdoor
   
• AngelList
   
• Monster.com
   

Apart from these job portals, you can also check the career pages of various companies, as many companies list their job openings on their websites. Networking with professionals in the industry and attending job fairs and events can also help you find data science jobs in India.

Salaries for a Data Scientist in India

An entry-level data scientist with less than one year of experience can anticipate a salary of around 3-5 LPA. A mid-level data scientist with 2-5 years of experience can earn around INR 6-12 LPA, and a senior data scientist with 5-10 years of experience can earn upwards of INR 15 lakhs per annum.

These are rough estimates and can vary heavily based on individual experience, skills, enterprise, location, job portals and company size. Finance and technology are the industries offering the highest-paying jobs for data scientists in India. Additionally, data scientists with machine learning, big data, artificial intelligence, and negotiation skills can earn higher salaries.

You can also check out Data Analyst vs Data Scientist here.

Frequently Asked Questions 

Is Data Scientist a good career in India?

Data Scientists are in high demand in India. In this advancing world, data is the new fuel for technical advancements, and thus, data scientists are required in various fields for growth. Data Scientist is among the most paying jobs in India. 

How can someone get into the field of data science?

To enter the data science field, build maths, stats, and computer science skills, and learn programming languages and machine learning algorithms. Gain practical experience with real-world projects, network, and consider a degree or certificate program.

What tools and technologies are used in data science?

The most commonly used tools and technologies in data science include programming languages like Python and R, and software like SAS, Power BI, and Tableau.

Is Data Science promising in the future?

Yes, data science is promising in the future as the demand for predictive analysis and automation continues to grow across industries. The field is expected to experience continuous job growth and offer excellent employment opportunities.

What are the advantages of Data Science over other fields of work?

The benefits of data science over other fields of work include high requirements for skilled professionals, excellent salary prospects, and the opportunity to work in a variety of industries.

Conclusion

In this article, we explored various things related to how to become a data scientist in India, leaving no stone unturned. We explored the roles and responsibilities, salary and perks, skills and experience required for the position, and preparation strategy to become a data scientist in India.
 

We believe this article on how to become a data scientist in India was helpful. To learn more about Data Science, check out our articles on:

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Happy Learning Ninja!