Artificial Intelligence (AI) is the development of theories, methods and techniques to develop algorithms that simulate human intelligence. The aim of Artificial Intelligence is to create machines capable of recording, learning, and analyzing data in order to predict events, trends, etc. Companies are increasingly using Artificial Intelligence to make predictions about their customers’ behavior, trends and changing habits. This allows them to improve their decision making, production strategy and thus increase their turnover.  

This is a real revolution! Today’s machines are capable of processing a very large volume of data of great complexity and quickly. Among the branches of AI, we find, for example, Machine Learning or Deep Learning, whose aim is to enable robots to learn autonomously. Automated vehicles are based on these new technologies.  

AI continues to progress, and this is not going to stop. More and more sectors are using these new technologies, including health, cybersecurity, finance, industry, etc.  

The person responsible for Artificial Intelligence within an organization is the Artificial Intelligence Engineer.  

The Data Analyst  

Data Analysts are trained to process, structure, cross-reference, categorise, analyse, and present data in the form of graphs and tables that are legible and coherent. Their objective is to advise businesses on their marketing decisions based on their analysis of the data. To do this, the Data Analyst inherits data recorded in Excel tables, for example, containing hundreds of lines and columns. Using languages such as SQL and Python, Hadoop or Spark, Data Analysts are able to arrange the data in such a way that a meaning emerges. Data that have been converted into comprehensible information have real added value for a business. Managers will be able to use them to improve their decision-making and, in doing so, increase turnover (business intelligence).  

The Data Analyst is the data technician. Their work is a combination of analytics and marketing.  

The Data Scientist  

The Data Scientist works upstream of the Data Analyst. While the latter works on data that have already been extracted, the Data Scientist must handle colossal volumes of data devoid of any structure or apparent logic, and which are totally disorganized. Its mission is precisely to model this shapeless mass of data, and to extract from it those which are potentially sources of information.  

Thus, the Data Scientist handles extremely large volumes of data. To do this, he or she must master complex IT tools, develop highly sophisticated algorithms and use mathematics and statistics. It is thanks to his work that Data Analysts will be able to analyze a smaller amount of data in their own field.  

The workload of a Data Scientist is therefore heavier than that of a Data Analyst.