Many of you often ask me about the different professions around the world of data, and the path one should follow to reach one of these professions. As this is a common question, I think that this post can help you to know these professions, as well as to know the trend and evolution of these professions in the last years.
First, the post gives a brief description of different analytical professions. These professional profiles have been extracted from the blog’s job postings. Among these professions stand out:
- Business Analyst: The business analyst is the person who has technical knowledge about the construction of analytical systems and at the same time understands and is aware of the needs of the business.
- Data architect: A data architect is a professional in data architecture, a data management discipline concerned with designing, creating, deploying, and managing an organization’s data architecture.
- Data Engineer: A data engineer is a worker whose primary responsibilities involve preparing data for analytical or operational uses.
- Data Analyst: A data analyst is the person who collects, processes and performs statistical analysis of the data. He or she translates numbers and data into business language to help organizations and companies understand how to make better data-based decisions for their business.
- Data Scientist: Data scientists are great data seekers, collecting and analyzing large structured and unstructured data sets. The role of a data scientist combines computer science, statistics, and mathematics.
- Machine Learning Engineer: Machine Learning engineers enter data into models defined by data scientists. They are also responsible for taking theoretical models from the data science and helping to scale production-level models that can handle terabytes of data in real time.
A common pattern of team organization and responsibility in Data & AI areas is as follows:
To see the weight of each of these professions in this sector, Google Trends data has been analyzed worldwide with respect to the number of mentions on the web of these professional profiles, so that it can be seen that there are four professions that stand out in the way of Business Analytics, Data Scientist, Data Analyst and Data Engineer, as can be seen in the following graphs:
The most mentioned professions in the network are Business Analyst, Data Analyst, Data Scientist and Data Engineer, which coincide with the most demanded in the job offers published in this blog.
As for the evolution of these professions, we can see how the professions of Data Analyst and Business Analyst are being equated in mentions, a logical situation due to the fact that many of the Data Analyst professionals already have a specialization in business and management of analytical projects, so both professionals will surely end up meeting. We can see the great increase in mentions of Data Scientist since 2016, and how Data Engineers are following the same upward trend, but not as pronounced because their boom has started a little later, around 2017 and 2018.
Another very interesting analysis is to look at the differences by different countries or geographical areas. We see in the following graph that worldwide the most commented profession in networks is Business Analyst, but in many countries this situation is not so, as is the case of Data Scientist that has greater mentions in countries like Russia, Spain and France.
I hope that this post serves to show the different professions around the data, as well as the demand for each of them according to the mentions in social networks.