No matter what job you have or seek, data science is essential. If you have not studied data science, or if you have not upgraded your skills, now is the time! Almost a decade ago, data science was mainly used by IT companies and a few consultancy companies to crunch data. But such has been its rapid influence that every enterprise in every industry today — no matter how large or small — requires people skilled in data science.
A 2020 LinkedIn Report ranked data science as one of the ‘top emerging jobs’ and that has not changed in 2022. In August this year, LinkedIn listed around 70,000 job listings for data scientists in India alone. The salaries for data scientists in India range from ₹4.5 lakh per annum to ₹25.2 lakh per annum, depending on experience level and domain expertise with an average annual salary of 10.6 LPA.
In recent years, Glassdoor has ranked data science as one of the 10 best jobs in the US based on median base salary, the number of active job openings and employee satisfaction rates. Harvard Business Review called data science “the sexiest job of the 21st century.” HBR further said that “high-ranking professionals with the training and curiosity to make discoveries in the world of big data” are in major demand.
What is data science?
Data science is an umbrella term under which specific, specialised roles are included like data analyst, data engineer, big data engineer, data architect, and cloud engineer/architect. Today, all data generated is digital, lending itself to number crunching and analysis in ways that were impossible before. In addition, analytic and visualisation tools have also improved, are easy-to-use, and are thus adopted widely.
Many industries would not have existed without data science. The services of e-commerce companies, online educators, taxi service aggregators, and even real-time maps that we use daily depend heavily on data science. Without data science, keeping track of customers and having an efficient supply chain would not have been possible. The growth of these sectors has been accelerated by AI and ML as well.
What does a data scientist do?
Data scientists design data modeling processes and create algorithms and predictive models to extract relevant data. They analyse this data, find insights, and share them with their team and business leaders. They apply data science techniques, such as machine learning, statistical modeling and artificial intelligence to both gather data and glean insights. Those who deeply understand the domain/industry in which their company operates can greatly influence the leadership and profitability of the company.
To break up what data scientists typically do: statistical analysis (to identify patterns in data), use machine learning (develop and implement algorithms and statistical models so that a computer automatically learns from the data), then apply the principles of artificial intelligence, database systems, human/computer interaction, numerical analysis, and software engineering.
Data scientists also need to know computer programming, in order to customise and analyse large datasets and find answers to complex problems. Data scientists should be able to write code in a variety of languages such as Java, R, Python, and SQL. Finally, data scientists need to find the story in the data and communicate it to the non-technical management of the organisation.
How to become a data scientist?
You could be a graduate of any stream, not necessarily technical (although, that helps), and in addition, can take up short courses offered by several institutions. Many free tutorials are available on the internet, if you would like to understand the subject before enrolling in a specific paid programme. With a paid programme, you will get access to a lot of personalised support in your learning journey, mentoring sessions with industry experts, opportunity to participate in hackathons, job fairs and hands-on learning through capstone assignments
A good combination of skills is advanced Excel for data crunching, SQL for framing queries, Python for data wrangling, and Tableau for good visualisation — once the process of data crunching is complete. You should also stay on top of changing trends to keep your skills up-to-date.
Upskilling and cross-skilling
As per a 2022 survey by Great Learning, more than 61.7 per cent (3 out of 5) professionals stated they are upgrading their skills in Cloud Technology (Azure, AWS, GCP). Fifty six per cent of professionals are learning MLOps and 55 per cent are learning Transformers. The most popular skill to acquire among professionals with 10+ years of experience is MLOps, with almost 73.1 per cent (3 out of 4) professionals learning techniques to scale Machine Learning models — one of the most pressing concerns in the industry.
This is followed by reinforcement learning (57.7 per cent), cloud technologies (57.7 per cent), transformers (57.7 per cent) and others. Professionals with 3-6 years of experience are more inclined towards learning cloud technology (71.7 per cent) as a core new skill followed by MLOps (62.3 per cent), transformers (60.4 per cent) and others.
As you can see, learning never ends. Due to the high demand for data scientists (90,000 jobs according to NASSCOM), salaries are also competitive, especially for those with technical skills, domain knowledge, and communication skills. Enrolling for a data science programme will prepare you for the future, regardless of whether you are a recent graduate or already employed in any other department like marketing or HR.
(The writer is Co-Founder, Great Learning.)