Do you have trouble understanding what sets the Data Analytics careers apart from each other? Learn the difference between the mission of the Data Analyst vs Data Scientist.
We help you create order by explaining you the differences between these two exciting careers.
Careers in Data Analytics
The ever-increasing growth of massive volumes of digital data stored in databases requires advanced skills of analysis. The aim is to understand the information concealed behind the raw data, so as to help business leaders make good decisions.
Data Analysts and Data Scientists are responsible for collecting data, processing it, analysing it and presenting it, once transformed into useful information, to their management.
The similarities between these two professions are therefore naturally numerous. They are responsible for analysing quantities of data that would otherwise be totally incomprehensible, and turning them into genuine sources of value for the company.
But they also differ. One could say that the Data Analyst is like the little brother of the Data Scientist, whose list of responsibilities is longer and more diverse.
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.
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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 disorganised. 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 analyse a smaller amount of data in their own field.
The workload of a Data Scientist is therefore heavier than that of a Data Analyst.
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