Data science is projected to grow from USD 37.9 billion in 2019 to USD 140.9 billion in markets by 2024. This means a compounded annual growth rate of 30 percent. Big data has permeated most aspects of our life. It not only captures our online behaviour but also keeps tabs on our health. The insights found with the help of data analysis have led to technological innovation in IT, automobiles, healthcare etc.


Now is a great time to start laying the foundation of your career in data with a masters in data science. You’ll learn applied statistics, machine learning, data visualisation, business information systems, data and predictive analysis, basic programming like R language and  SQL, cloud computing etc.

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Data Scientists need a keen eye for detail, an analytical bent of mind and problem-solving aptitude. The soft and hard skills combined help data scientists land high paying jobs across industries. Data Science has branched off into many specialised roles and job profiles in the past few years. Let’s take a look at a  few of them:



Data Scientist – Data Scientists are generalists. Their job includes a wide array of roles and responsibilities within data science. They can be responsible for data collection, analysis, visualisation and business intelligence. As a jack of all trades, a data scientist can also take on managerial roles to lead a team of several specialists.

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Data Analyst – Some responsibilities of data scientists and analysts might overlap. At times, all data scientists are hired for doing is data analysis. Data Analysts should be proficient in data cleaning, R, Python, machine learning, SQL, MATLAB and finally, data visualisation. They collect, refine and analyse large data sets to help businesses solve problems.


Business Analyst – Business Analysts are the glue that holds stakeholders, users and management together. They help businesses streamline processes and weed out redundancies. Along with communication and negotiating skills, business analysts should also be skilled in the basics of languages like R, Python and SQL. Other technical skills include data visualisation, mining and analysis.

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Data Engineer – Data Engineers are responsible for building and maintaining the systems and platforms that collect, store and analyse data. They build databases and their architecture. Their end goal is to help data scientists gain insights from data sets and help businesses solve problems more efficiently. Data Engineers must have a firm grasp of programming languages like Python, R and several business intelligence tools and software frameworks like Hadoop.


Machine Learning Specialist – Getting a machine learning certification is essential for someone aspiring to become a machine learning specialist. Machine learning is the process of algorithms evolving independently without being specifically programmed to do so. An ML specialist will help develop such algorithms to analyse data and predict.

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Database Administrator – They use software related to databases to collect and store data efficiently so data scientists and data analysts can analyse it. They must know about relational databases (RDBMS), SQL, NoSQL, MySQL, computing architectures, networking and other storage technologies.


This was a brief about the roles and responsibilities of data scientists. As data science evolves, the job profiles will also evolve into several areas.


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