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Data Scientist vs Data Analyst : What are the main differences ?

What is the difference between a Data Scientist and a Data Analyst

Data Scientist vs. Data Analyst: Discover all the differences between these two key positions in Data Science. Tasks, skills, salary, training, etc. Here you will find a detailed comparison between these two professions in the field of Big Data.

The professions of Data Scientist and Data Analyst are among the most sought-after in the field of Big Data and Data Science. However, these two roles are often mistakenly confused with each other. There are key differences between the Data Analyst and the Data Scientist. Here are the most important ones.

Data Scientist vs. Data Analyst: the most important differences.

As the title suggests, a data analyst’s job is to analyse data. In addition, they have technical know-how and skills in “data visualisation”. The data scientist goes one step further and has even more extensive programming skills.

Often, analysis focuses on data that comes from a single source, such as a CRM system. A data scientist, on the other hand, examines data from a variety of unrelated sources.

While a data analyst merely processes the tasks set by his company, the data scientist identifies questions himself, the processing of which will be of great benefit to the company. In addition, the Data Scientist excels in the development of statistical models and the mastery of Machine Learning.

In summary, the Data Scientist can be understood as a more advanced form of the Data Analyst. The data scientist has more freedom and must demonstrate greater creativity and technical expertise.


Both the profession of data analyst and that of data scientist require comprehensive knowledge of mathematics and software technology, a basic understanding of algorithms and a certain talent for communication.

The data analyst uses the programming languages Python, R, SQL, HTML and JavaScript. He also uses spreadsheet programs such as Excel and data visualisation tools such as Tableau. He is proficient in SQL and has a scientific curiosity that enables him to tell a story based on the data.

For his part, the data scientist has all the skills of the analyst in terms of modelling, analysis, mathematics, statistics and computer science. In addition, however, he or she brings other professional competences with him or her.

In addition to the languages used by the Data Analyst, the Data Scientist uses SAS, MatLab, Pig, Hive and Scala. This also gives him the ability to understand business problems and communicate his findings to IT teams and management using Dataviz.

The Data Scientist is able to influence the way an organisation addresses the challenges it faces. Furthermore, the Data Scientist uses distributed computing frameworks such as Hadoop and has valuable machine learning skills.

Areas of responsibility

A data analyst has to write SQL queries to find solutions to his company’s challenges. He sifts through and analyses the data available to the company to identify correlations and discover trends.

His role is also to identify data quality issues and implement new metrics to better understand business performance. He coordinates with the data engineering teams to compile new data. Finally, he designs and creates data reports using various “reporting” tools to help his company make better decisions.


Are you interested in a career change into Big Data, but don’t know where to start? Then you should take a look at our Data Science training course


Are you interested in a career change into Big Data, but don’t know where to start? 

Then you should take a look at our Data Science training course

The Data Scientist has more responsibility. His or her job is to use data to create new services and products, new ways and opportunities for the growth and development of his or her company. In this way, the data scientist can identify problems and challenges that can be solved with the help of data.

He is also responsible for cleaning and structuring the data so that it is suitable for analysis. If the data sets are scattered or disjointed, it is the analyst’s job to fix the problem by creating some uniformity. He also has to develop new analysis methods and machine learning models.

As the title suggests, the Data Scientist is a scientist. Therefore, he has to conduct experiments and tests on a daily basis. Finally, he creates reports and data visualisations from the results of his analyses, which he presents to the management in the form of a clear and understandable narrative.


The Data Scientist has more responsibility than the Data Analyst and has more comprehensive skills. Therefore, it is not surprising that his salary is higher.

The average salary of a data analyst in the US is around $60,000 per year, according to PayScale, Glassdoor and In France, it varies between €37,000 and €65,000 per year, depending on experience level, according to our survey of CAC 40 companies.

However, the average salary of a data analyst depends strongly on their specialisation: Financial analyst, market research analyst, business analyst… As a rule, financial analysts are the best paid specialists.

As far as the Data Scientist is concerned, the average annual salary in the USA is over 100,000 US dollars, according to Glassdoor, Payscale and Indeed. In Germany, the salary of a Data Scientist ranges between 42,000 and 57,000 euros per year. An experienced expert in the field of data science can expect a salary between 60,000 and 80,000 euros per year.

So, at first glance, the salary difference between these two professions seems to be much less pronounced in Germany than in the US. While the Data Scientist earns twice as much as the Analyst in the US, their salaries in Germany would be almost identical!

However, many European companies employ data analysts under the job title of data scientists. This lack of clarity contributes to the fact that the theoretical average salary is lower. In practice, data scientists usually receive a significantly higher salary.

Further Education

The profession of data analyst is easier to learn than that of data scientist. DataScientest, for example, offers further training to become a data analyst, which is aimed at people with a bachelor’s degree with business or science lectures and knowledge of marketing and statistics.

For our Data Scientist training a Bachelor’s degree in Mathematics or Statistics or an equivalent level of education in science is advised. Solid communication skills are also required.

Each of these courses is offered as an intensive course (bootcamp) or as part-time training with an innovative “blended learning” approach that combines distance learning and face-to-face teaching. Upon successful completion of your training, you will receive a certified degree from Sorbonne University. Don’t wait any longer, discover all our Data Science courses.

You now know the differences between Data Analyst and Data Scientist. Discover here our complete overview of Data Science.

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