The most in-demand data skills

There is one thing that cannot be denied, and that is that the volume of data that is generated each year is increasing. With this in mind, it really should come as no surprise that employers worldwide are increasingly becoming reliant on data, and the demand for those who are data science professionals and work in the field of data analysis is more significant than it has ever been. 

Specialist recruitment agencies like Agile Recruit are seeing an increasing demand from employers for a range of individuals with data skills who can work in this field. With the current demand for employees in this specialism outstripping the pool of suitable candidates, this is excellent news. Anyone with the right data skills could find themselves being called by recruiters regularly, and the potential for better salary opportunities is undoubtedly there.

If you are looking to upskill to make yourself more desirable to potential employers, then the question you might ask is, what are the most in-demand data skills? Let’s take a list of the most in-demand data skills that employers are looking for at the moment. 

Data management

Data management refers to the process that involves the collecting, keeping and using of data in a manner that is efficient, secure and cost-effective. This is a skill that has become even more vital as a result of data protection laws like GDPR and data privacy. 

Those individuals who understand how both cloud-based databases and those in physical environments work will be able to boost their careers.


This is the mathematical and statistical analysis of economic data as a basis for any financial forecasting. For roles in the financial sector, this is an essential skill. It can help with a career in investment banking or hedge funds. 

Machine learning

This is a branch of AI, Artificial Intelligence, focused on using data and algorithms to replicate how humans learn. It improves its accuracy as time progresses. Machine learning is used to seek out patterns in big datasets.

Whilst a data analyst isn’t often expected to have mastered machine learning, developing skills in this field can help anyone looking to become a data scientist.

Statistics and probability     

When it comes to machine learning algorithms for data science, statistics are vital. They are used to capture and then translate patterns in data into actionable evidence. A data scientist uses statistics and probability to collect, review and then analyse data before using it to conclude. 

Solid skills in this field mean being better able to avoid logical errors and bias in data analysis, identify data patterns and create more reliable and accurate results.


Standard Query Language is a language for programming that allows an individual to access and manipulate databases. They can organise, query and also update data that is stored in relational databases and also modify data structures (schema)

It is common for interviews for data analyst roles to include some form of technical screening with SQL, so this is an important skill.

Statistical programming

This is the language used to help perform the tasks that data scientists need to make sense of the data that is usually written in code. Python and Rare two of the most commonly used statistical programming languages. 

These open source languages assist scientists and analysts in cleaning, analysing and then visualising large sets of data more efficiently. 

Putting in the time to ensure that you have these sought-after skills as part of your skill set can help improve your chances of securing a role in data analysis.

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