As many as 84.4% of professionals say that recruiters look for machine learning (ML) as the most crucial skill when hiring a data scientist, followed by statistics at 78.9%, reveals Data Science Skills Survey 2022 by global edtech company for higher and professional education Great Learning.

Further, two out of three professionals with 0-3 years of experience said recruiters consider data visualisation as a must-have skill. This number reduces for respondents with more years of experience.

Basic skills to make a career in data science

As per the survey, nine out of 10 data science professionals mentioned that knowledge of programming language (R, Python, SAS) is among the most basic skills to start a career in data science.

This is followed by knowledge of statistics (80.6% of respondents) and basic ML (75.6% of respondents). More than three in four professionals claimed that basic ML understanding is a must-have skill for a career in data science and is indicative of how far we have come in the field.

Popular programming languages amongst data scientists

Out of the host of programming languages, 90% data science professionals use Python as their choice for statistical modelling.

Beyond that, SQL and R are preferred by 52.8% and 38.3% of the respondents, respectively.

The use of SQL (68.4%) is highest in retail, CPG, and ecommerce, followed by IT at 62.9%. R is the most commonly used programming language in the pharma and healthcare sector, with three in five (60%) professionals claiming they use it for statistical modelling.

Top data visualisation tools

Despite all the technological advancements in data science, the use of MS Excel remains high with 2 in 3 (63.3%) professionals using it regularly. This is followed by Tableau (56.7%), Power BI (43.9%), and QlikView (12.2%).

When it comes to experience levels, MS Excel (84.6%) is mostly used by professionals with more than 10 years of experience. Tableau is the preferred choice for mid-senior career data scientists with 6-10 years of experience. Sector-wise, Tableau is the most popular tool in pharma and healthcare, and IT. Apart from that, MS Excel remains the highest used tool for data visualisation in all the other sectors surveyed.

Continuous learning a necessity

As data science proves to be a critical proficiency to ensure the company’s development, 98.6% of respondents agree with the need for continuous upskilling in the field.

Three in four data science professionals with less than three years of work experience engage in upskilling weekly, while more than 50% professionals in the 3-6 years bracket upskill weekly.

Professionals with 6-10 years of experience prefer to upgrade their skills quarterly.

Data scientists upskilling in cloud, MLOps, and transformers to stay relevant

More than three out of five (61.7%) data scientists believe upgrading skills in cloud technologies is crucial followed by MLOps (56.1%) and transformers (55%) to remain relevant to the industry's current needs.

Three in four professionals with 10-plus years of experience are learning MLOps to upgrade their skill sets. Mid-career professionals with 3-6 years of experience are learning cloud technologies (71.7%) as a core new skill, followed by MLOps (62.3%), transformers (60.4%), and others.

Sector-wise, professionals in retail, CPG, and ecommerce are more inclined to learn cloud technologies (73.7%) as a new skill. As many as 70% of professionals working in BFSI upskill in MLOps.  Another 70% and 60% of professionals in the pharma & health sector are interested in learning transformer and computer vision as core skills.

“The data science domain is on track to change the nature of jobs, now and in the future. For professionals looking to thrive in a digital-driven future, setting a foundation for the right data science skills needs to start now to weather the changes that are to come. This domain has impacted the way companies and businesses function and professionals who upskill in this field have a chance to become prime candidates in lucrative industries,” Hari Krishnan Nair, co-founder, Great Learning said.

“Through this survey, we aim to help recruiters, industry leaders, policymakers, companies, and data science experts/aspirants gain an in-depth understanding of the most impactful languages, models, tools, skills, upskilling approaches and recruiter perspective that is needed to make structured and informed decisions,” he added.

The report has been developed after primary research through a survey conducted amongst data scientists and leading AI/ML practitioners. This was complemented by direct discussions with job-seekers to understand and gauge their perspective on the in-demand skills in this domain.

The participants were also interviewed on the critical skills expected by recruiters from professionals of all experience levels while hiring.