Data-related professions are often a source of misunderstanding and are also sometimes subject to a hierarchy, which is wrong, since they are quite different professions. The uninitiated, also known as Muggles in the industry, can see some grey areas.
Indeed, the mistake often made is to use the term ‘Data Scientist‘ indiscriminately to encompass all data-related professions, when in fact they are very different roles.
The aim of this article is to lift the veil on the main data professions, and to better understand their differences and specificities. What’s more, at the end of this article you’ll find a diagram illustrating the different professions…
Data Analyst
As the name suggests, the Data Analyst is the person responsible for analysing data in order to derive indicators that will be used by the company’s managers to help them make strategic decisions. As you can see, this is a key role within a company. These indicators will be materialised in the form of graphs or tables that can be understood by decision-makers.
The Data Analyst will use business intelligence software such as Qlik or Power BI. They will also need to be comfortable with SQL, as well as mastering the Python and R languages.
Data Engineer
This profession, which is still relatively unknown, was often confused with the Data Scientist, and as a result there are now more and more job offers, but few qualified profiles for this position. The data engineer (a term which, incidentally, can frighten muggles) is the person responsible for retrieving data.
The sources may be diverse: websites, applications, surveys, etc. He or she must ensure that the data is made available, secure and high-performance, so that it can be used. Of course, the Data Engineer must be comfortable with the enormous volumes of data that can potentially be generated. Because of their relative proximity to other professions, they will also need a grounding in Machine Learning or algorithms.
They will be using technologies such as Hadoop and Spark. You will also need to be very comfortable with SQL, NoSQL and Neo4j DBMS, as well as Python and Scala programming languages.
Data Scientist
Less than 10 years ago, Linkedin listed barely a hundred job offers for Data Scientists; today, there are several thousand. That just goes to show how popular this job has become. With a more scientific profile (mathematics or science) than the Data Engineer or Data Analyst, their primary role is to analyse the data provided to them in greater depth, in order to draw conclusions and make predictions about future behaviour. Unsurprisingly, this is a very important role within a company for future decision-making.
He or she will use the R (very science-oriented), Python and Matlab languages, as well as libraries such as Scikit-Learn or PyTorch.
Machine Learning Engineer
The Machine Learning engineer is an evolution or bifurcation of the Data Scientist profession. Their role will be to optimise and maintain the algorithms developed by the Data Scientists using the data prepared by the Data Engineers. The ML Engineer is therefore a demanding job, requiring solid skills in development, algorithms and mathematics. They must also be able to explain their work to their managers or other people who are not experts in this field.
ML Engineers must master several programming languages, such as Python, R, C, Scala, Matlab and Java. They will work mainly in cloud environments such as Azure, and on platforms such as Rapidminer.
Conclusion
As you will no doubt have realised, these professions are increasingly sought after by companies, as we live in an age where data is omnipresent and its importance paramount. However, in order to carry out these activities, it is vital to be properly trained.
