As more organisations across sectors awaken to the far-reaching benefits of having skilled data professionals on board, the demand is projected to grow by 28% by 2026, notes Subramanyam Reddy, founder and CEO, KnowledgeHut, a global edtech firm.
Enterprises across sectors — ranging from automobiles, defence, logistics and banking to agriculture, healthcare, food and beverage and more — have realised the ability to leverage data generated by their users to influence a wide range of desirable outcomes, in turn, fuelling the Data Science boom.
Organisations are relying on a data-first practice to remain relevant and competitive in modern times. But a lack of data literacy can seriously slow down business growth.
With disruption becoming more than a buzzword, there’s a need for data scientists who can actually wrangle data, create models out of it, and back business decisions at a higher level.
By 2025, Asia is estimated to have 170 zettabytes (1 zettabyte is 1 trillion gigabytes) of data, of which, enterprises will contribute 60%.
Clearly, data is revolutionising the way enterprises operate.
As more organisations across sectors awaken to the far-reaching benefits of having skilled data professionals on board, the demand is projected to grow by 28% by 2026.
Here are the top five Data Science roles in demand across industries today:
#1. Data Analyst
Professionals who are experts in data analytics are in high demand as organisations are looking for methods to harness the power of Big Data.
Data Analyst is all about collecting, cleaning and interpreting data in order to provide a solution to any problems.
For instance, healthcare organisations use data analytics to provide insight into clinical data, higher-quality care and improve patient outcomes. Based on the data, they are able to significantly reduce the number of patient hospitalisations and ER visits.
These professionals evaluate, optimise, and improve business strategies for organisations and outsmart its competition using data analytics.
In order to become a Data Analyst, a candidate needs to have some of these top skills such as Structured Query Language (SQL), Microsoft Excel, critical thinking, Python-Statistical Programming, data visualisation, presentation skills and Machine Learning to name a few.
Average salary in India for a Data Analyst ranges between ₹3.3 lakh at the entry-level to ₹11 lakh for a senior professional where the average annual salary for an experienced professional is ₹20 lakh.
#2. Data Scientist
Business leaders are paying heed to the value that data scientists bring to organisations.
From extracting insights from large amounts of data generated to communicating and demonstrating the value of the organisation’s data, data scientists look for trends and patterns to facilitate improved decision-making processes across the enterprise.
These professionals also make projections to predict future needs using predictive analytics.
Businesses can leverage insights from data to direct their strategy and attract new customers.
Some of the technical skills required to become a data scientist are statistical analysis and computing, machine learning, deep learning, processing large data sets, data visualization, data wrangling, mathematics, programming, statistics and big data among others.
Along with the technical skills, these professionals are also required to have non-technical skills such as strong communication skills and strong business acumen.
Average salary for a data scientist in India is ₹10.6 lakh per year.
An experienced data scientist earns about ₹20 lakhs per annum.
#3. Data Engineer
Data engineers create the foundation that data analysts and scientists build on.
Data engineers can be considered as the backbone of a company as they are responsible for building, designing, and managing large databases. They are in charge of constructing data pipelines using complex tools and techniques to handle data at scale.
In a nutshell, a data engineer shares his insights with the company through data visualisation, which in turn help the business grow.
Some of the technical and soft skills that a data engineer should possess to perform their responsibility efficiently and effectively are coding, data warehousing, knowledge of operating systems, database systems, data analysis, critical thinking skills, basic understanding of machine learning and communication skills to name a few.
Professionals who work as big data engineers take home, on average, ₹834,000 per year. Experienced professionals earn about ₹18 lakh.
#4. Business Intelligence Analyst
Today, businesses are increasingly turning to Big Data to drive business strategy.
Data collected from users is analysed and key insights extracted to inform the strategy and give customers what they need.
The role of a business intelligence analyst is more technical in nature as they serve as a bridge between business and IT. These professionals require more knowledge of a specific industry and industry trends.
One of the best examples for how data science is informing business strategy is that of Netflix.
Not only does Netflix use recommendation systems for suggesting movies or shows to its customers, it also leverages data to decide what artworks should be displayed for movies and shows, what are the best frames from a show that editors should work on, how can they improve the Quality of Service (QoS) streaming and how they can optimise different stages of production.
With less than 1 year of experience to 8 years, the average salary of business intelligence analyst in India ranges from ₹3.5 lakh to ₹16.4 lakh with an average annual salary of ₹7 lakh. Experienced professionals earn on average ₹19 lakh per annum.
#5. Quantitative Analyst
To identify profitable investment opportunities and manage risk, organisations such as investment banks, hedge funds and private equity firms look for quantitative analysts to make informed decisions in the complex and changing environment.
Very often, the secret to improving business processes and outcomes is already within reach of companies in the data that they possess.
Once data is analysed and trends are revealed, the next step is to identify opportunities for further growth and improvement.
Quantitative analysts should possess expertise in mathematics and statistics, data mining and data analysis, extensive financial knowledge, and Programming skills.
The demand for high performing business intelligence analysts will certainly continue to grow.
With the challenging nature of work, they are generally very well compensated, especially in hedge fund firms.
Upskilling is the need of the hour
Despite an increase in the number of junior-level candidates, high-paying data science skills are still in shortage.
Job roles in data science are in high demand, but cannot be filled by undergraduates with no experience.
Bootcamps are a great way for enterprises to harness the power of data to unlock business value.
It’s an effective way for businesses to invest in forward-thinking data talent, craft smart business strategies, and drive informed decision-making.
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