Top Skills In Demand: Data Analytics

A career in Data Analytics can be a success, as long as you have the skillset to match. Each year, data analysts are becoming more and more in demand, therefore, current and aspiring professionals must develop their skills. We’ll take a look at the skills in demand and how you can begin to develop these skills. 

Machine Learning 

In recent times, the implementation of AI and Machine Learning in particular, has become a major development in data science. It allows professionals to build algorithms of the sole purpose of finding patterns in big data sets, improving their accuracy over time.

As a result of more data which the machine learning algorithm possesses, it becomes “smarter”, which in result, produces more accurate predictions.

Although data analysts are not expected to be machine learning experts, but by developing your skillset, this gives you a competitive advantage among the rest. 

Probability and Statistics 

Estimates and predictions form an important part of Data science. With the help of statistical methods, we make estimates for the further analysis.

As a data analyst, your main duties will likely include: 

  • Collecting & analysing data 
  • Interpreting data 
  • Presenting data 

Data is becoming an increasingly important part of business strategy, as it allows them to gather insights, which in result, allows them to personalise customer interactions. 

Professionals with a strong understanding in probably and statistics allows them to identify specific patterns in data, produce accurate results and avoid errors in their analysis. 

The University of London provide an online course for those who wish to begin learning more about probability and statistics. 

Statistical Programming 

Unlike Microsoft Excel, statistical programming languages such as Python, allow you to perform advanced analysis. Professionals are who able to write programs in such languages, will benefit from being able to clean, visualise and analyse larger data sets more accurately. 

It is recommended that you learn either Python or R, as both can successfully accomplish similar data tasks. R is to be specifically for analytics, however, Python is hugely popular and considered to be the easier language of the two, when it comes to learning. 


Also known as Structured Query Language, SQL is a popular database language, which is used to communicate with databases. SQL users can benefit from updating, organising and executing queries stored in relational databases. 

It is common for data analyst candidates to include an SQL test, to review their skill level. So it is important that you are familiar with SQL, because in order to get access to the company database, you will need to know SQL. It is the most important skill to have, as well as being perhaps one of the easiest to learn. 

Data Management 

Although businesses will have specific roles assigned to data management, which include: data architects and engineers or database administrators – data analysts can also manage data. 

In order to manage data, professionals must understand the practices of collecting, organising and storing data in a way that is efficient and secure – as well as cost effective. It’s important to remember that different companies are likely to use different, however, by developing your skillset, you will develop a better understanding of how databases work. 

International Business Machines, IBM, are presenting an “Introduction to Data Engineering” course for professionals who wish to learn more on the core concepts, processes, and tools you need to know in order to get a foundational knowledge of data engineering. 

Incorporating your data analyst skills into your application 

It’s a great idea to clearly display your data analytics skills on your CV under the “skills” section; whilst highlighting any specific skills under that section. Including any related qualifications will also be a benefit for potential employers. 

As well as your CV, to demonstrate your knowledge further, you can elaborate on your skills during the interview. Speak about how and why you used the skills in previous positions. This shows you can demonstrate context and logic.