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10 Most Sought After Data Science Skills In 2021

For professionals who are setting out to skill themselves in data science, it is essential to understand the requirements of the industry and cultivate a skillset to bridge this gap.

Every day about 1.145 trillion MB of data is generated globally. Companies across industries are looking for talent to process, assess, and filter relevant information for their business from this ever-increasing mass of data. With the rapid advancement in technology, more companies are digitising their operations, leading to a growing demand for professionals with both the technical capabilities and the business acumen to leverage the wealth of data at our disposal today. 

For professionals who are setting out to skill themselves in data science, it is essential to understand the requirements of the industry and cultivate a skillset to bridge this gap.


The most in-demand skills and tools for data science professionals in 2021 are:

  1. SQL: Structured Query Language (SQL) is used to communicate with and extract data types from databases. A data analyst needs to know SQL as they will need it to access information from a company’s database. Hence, it becomes the most critical skill for a data science professional. Learning SQL is beginner-friendly and does not require prior knowledge of databases or programming languages. 
  2. Python: Created in the 1990s, Python is seen as the primary language that every data science professional must know and is easy to learn compared to other languages. Data science professionals use Python for application development, statistical programming (to clean, analyse, and visualise large data), web development, dynamic binding, dynamic typing and web scraping, among other tasks. 
  3. R Programming: R is a free open source software used to extract, reshape and analyse information from a large chunk of data. Data scientists, data miners, and statisticians use R for statistical data analysis and machine learning visualisation. This programming language is leveraged for data analysis across industries like healthcare, banking, IT and e-commerce.
  4. Machine Learning: Machine learning is a branch of Artificial Intelligence (AI) that helps engineers create programs and develop robust data analytics algorithms that enable machines to emulate human intelligence. Today, machine learning is in high demand as it is used to develop systems that can predict the course of events by finding patterns in big data sets and help draw conclusions based on data matrices. 
  5. Deep Learning: Deep learning is a subset of machine learning and a must-have skill to master to have a career in data science. Deep learning is mainly used for speech and image recognition, NLP (Natural Language Processing) and robotics. With deep learning, data science professionals can upscale their careers in defence, industrial automation, medical research and electronics, among other sectors. 
  6. Spark: Created in 2014, Spark is a framework of unified computing engines and a set of libraries for parallel data processing. It is the most actively developed open-source engine for data processing of big data. It supports multiple programming languages like Python, SQL, Java, and R. Spark makes it easy to start with and scale up to big data processing and runs anywhere from a desktop to clusters of thousands of servers. 
  7. Data Visualisation: The use of visual representation like graphs and charts to communicate data insights often allows for greater clarity and identification of patterns. Though data visualisation may not be a critical skill that job descriptions ask for, knowing how to present your work and visually showcase analysis and insight is considered a baseline for data science professionals. Tableau is one of the most popular data visualisation tools used by data scientists. This tool supports numerous data sources and allows the transformation of analyses into dashboards for colourful visualisation, making creating data models and reports more convenient. Hence, it is a widely accepted tool as it offers flexibility to data scientists.
  8. Cloud: Cloud skills for data science professionals are in high demand as organisations shift their IT infrastructure to the cloud, especially with the pandemic-induced move to work-from-anywhere models. The primary skills to be well-versed in the cloud are Amazon Web Services (AWS), Java, Azure, Linux, DevOps, Docker, and Infrastructure as a Service (IaaS). Cloud computing is expected to grow in the coming years as more and more companies migrate their operations.
  9. Mathematics and Statistics: Having sound knowledge of calculus, linear algebra, statistics, and probability is a must for data analysis, data sorting, and data visualisation. A statistician is responsible for collecting, analysing and interpreting data, which will then be communicated to stakeholders, thereby contributing to the operational strategies of an organisation.

And most importantly,

Business Acumen: A survey conducted by edtech platform Scaler found that over 80% of data scientists struggle initially in their careers as real-world datasets are far more fragmented, non-standard and complex than the samples they work with while in training. More than 95% of respondents of this survey correctly highlighted the need for data scientists to solve open-ended business problems, which requires practical experience, in addition to training and simulation. Business Acumen and intelligence, therefore, is very critical for a data scientist to work effectively.


According to job search engine Indeed, job searches for Data Scientist roles are on the rise in India, increasing by 35 per cent between July 2020 and July 2021. Their data also highlights a 50 per cent increase in searches for Business Intelligence Developers, a role equally critical in aiding an organisation’s decision-making process.

It is pretty clear that Data Science is THE industry to be in right now for technology professionals across the globe. All they need to succeed and build a solid career are the right skills for the job.

Disclaimer: The views expressed in the article above are those of the authors' and do not necessarily represent or reflect the views of this publishing house


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