Data is a hot topic. With the volume of data, we are generating rising year on year, and so many companies across the board becoming reliant on it, it’s no surprise that the demand for data analytics and data science professionals is off the chart.  

Cloudflower has shown a shortage of data scientists and analysts, with demand outstripping availability – meaning the vast majority of data scientists will receive recruiting calls regularly.  

But what skills do these people have that are so in demand at the moment? Perhaps you could upskill and make yourself more desirable in the job market if you knew that.  

Well, now you can. We’ve taken a look at research on the topic and our job openings for the last few months to pull together this list of the skills employers are seeking most.  

#Data Management 

Data Management is the name given to the process of collecting, keeping and using data efficiently, securely and cost-effectively. Data management is becoming even more critical with the increasing advent of data privacy and protection laws such as GDPR.  

Understanding how different databases work, both in the cloud and physical environments, will help you in your career.  

#Econometrics 

Econometrics is the statistical and mathematical analysis of economic data, acting as a basis for financial forecasting. Understanding econometrics is vital if you look for jobs in the financial sector, such as hedge funds or investment banks.  

#Machine Learning 

Machine learning is the branch of Artificial Intelligence (AI) that focuses on using algorithms and data to imitate the way humans learn, improving its accuracy every time. It is used to find patterns in big data sets.  

Data analysts aren’t usually expected to have machine learning mastered, but it could help develop your skills in this area if you want to become a data scientist.  

#Probablity & Statistics 

Statistics are at the core of machine learning algorithms in data science, as they are used to capture and translate data patterns into actionable evidence. Data scientists will use probability and statistics to gather, review and analyse data and draw conclusions.   

Having a solid skillset in probability and statistics means you will be better able to avoid bias and logical errors in your data analysis, identify patterns in the data, and produce more accurate and trustworthy results.  

#SQL 

SQL (Standard Query Language) is a standard programming language that lets you access and manipulate databases. It allows you to organise, query, and update data stored in relational databases and modify schema (data structures). 

It is pretty standard for data analyst job interviews to include a technical screening using SQL, so learning it is an important skill to have.  

#Statistical Programming 

Statistical programming is the language used to perform tasks that help data scientists to make sense of data – usually by writing a code. Two of the most commonly known statistical programming languages are Python and R. 

Statistical programming languages are open source languages that help analysts and scientists clean, analyse and visualise large data sets more efficiently.  

#Statistical Visualisation 

Statistical data visualisation is the drawing of graphic displays to show data. It is helpful for data cleaning, exploring data structures, detecting unusual groups, identifying trends, spotting local patterns, evaluating modelling output and presenting results.  

Statistical visualisation helps data analysts to tell stories that enable better-informed business decisions.  

Putting in the time and effort to learn some of these technical skills could help to make your data analyst and scientist career take off.  

If you are ready to boost your career, give the experts at Agile Recruit a call.  

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