10 most in-demand data science skills in 2021

For professionals looking to advance in data science, it is essential to understand the industry requirements and cultivate a skill set to bridge this gap.

Every day, approximately 1.145 trillion MB of data is generated around the world. Companies across all industries are looking for talent to process, assess and filter information relevant to their business from this ever-growing mass of data. With rapid advancements in technology, more and more companies are digitizing their operations, resulting in a growing demand for professionals with both the technical capabilities and the business acumen to take advantage of the wealth of data at our disposal. today.

For professionals looking to advance in data science, it is essential to understand the industry requirements and cultivate a skill set 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 and extract data types from databases. A data analyst must know SQL because he will need it to access information in 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 any prior knowledge of databases or programming languages.
  2. Python: Created in the 1990s, Python is considered the primary language any data science professional should know and is easy to learn compared to other languages. Data science professionals use Python for application development, statistical programming (to cleanse, analyze, and visualize big data), web development, dynamic binding, dynamic typing, and web scraping, among other tasks. .
  3. R programming: R is free open source software used to extract, reshape, and analyze information from large amounts of data. Data scientists, data miners, and statisticians use R for statistical data analysis and machine learning visualization. This programming language is used for data analysis in industries such as 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 analysis algorithms that allow machines to emulate human intelligence. Today, machine learning is in high demand as it is used to develop systems capable of predicting the course of events by finding patterns in large data sets and helping to draw conclusions based on matrices of events. data.
  5. Deep learning: Deep learning is a subset of machine learning and a necessary skill to master for a career in data science. Deep learning is mainly used for speech and image recognition, NLP (Natural Language Processing) and robotics. Through deep learning, data science professionals can advance their careers in defense, industrial automation, medical research, and electronics, among other industries.
  6. Spark: Created in 2014, Spark is a unified compute engine framework and set of libraries for parallel processing of data. It is the most actively developed open source engine for processing big data. It supports multiple programming languages ​​such as Python, SQL, Java and R. Spark makes it easy to start and scale big data processing and runs anywhere from desktop to desktop. clusters of thousands of servers.
  7. Data visualization: Using visual representations such as charts and tables to communicate information about the data often allows for greater clarity and identification of patterns. While data visualization may not be a core skill demanded by job descriptions, knowing how to present your work and visually present analysis and insight is considered a benchmark for data science professionals. Tableau is one of the most popular data visualization tools used by data scientists. This tool supports many data sources and enables the transformation of analyzes into dashboards for colorful visualization, making it easy to build data models and reports. Hence, it is a widely accepted tool because it provides flexibility for data scientists.
  8. Cloud: Cloud skills for data science professionals are in high demand as organizations move their IT infrastructure to the cloud, especially with the pandemic-induced shift to work-from-anywhere models. The main skills for mastering 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 companies migrate their operations.
  9. Mathematics and Statistics: Having a good knowledge of calculus, linear algebra, statistics and probability is essential for analyzing, sorting and visualizing data. A statistician is responsible for collecting, analyzing and interpreting the data, which will then be communicated to stakeholders, thus contributing to the operational strategies of an organization.

And especially,

Business acumen: A survey by the edtech Scaler platform found that over 80% of data scientists struggle early in their careers because real-world datasets are much more fragmented, non-standard, and complex than samples. they work with during their training. Over 95% of respondents to this survey correctly pointed out the need for data scientists to solve open business problems, which requires hands-on experience, in addition to training and simulation. Business acumen and intelligence are therefore very essential for a data scientist to work effectively.

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

It’s pretty clear that data science is THE industry tech professionals around the world need to be in right now. All they need to be successful and build a solid career are the right skills for the job.


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Sam D. Gomez