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

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

Discover all the differences between these two key positions in Data Science : Data Analyst and Data Scientist. 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.

Do they have the same objectives and way of working?

As the title suggests, a data analyst’s job is to analyze data. In addition, they have technical skills and many skills in data visualization. The data scientist goes one step further and has even more extensive programming skills.

Often, the 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 independence and must demonstrate greater creativity and technical expertise.

What Skills distinguish a Data Analyst from a Data scientist?

Both the profession of data analyst and that of data scientist require comprehension, 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 visualization 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 modeling, analysis, mathematics, statistics, and computer science. In addition, however, he brings other professional skills with him.

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 can influence the way an organization faces challenges. Furthermore, the Data Scientist uses distributed computing frameworks such as Hadoop and has valuable machine-learning skills.

What are the responsibilities of a Data Analyst and Data Scientist?

A data analyst has to write SQL queries to find solutions to his company’s challenges. He sifts through and analyzes 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.

The Data Scientist has more responsibility. His job is to use data to create new services and products, new ways, and opportunities for the growth and development of his 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 must conduct experiments and tests every day. Finally, he creates reports and data visualizations from the results of his analyses, which he presents to the management in the form of a clear and understandable narrative.

What can you expect to earn as a Data Analyst or Data Scientist?

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 Salary.com. In France, it varies between €37,000 and €65,000 per year, depending on experience level, according to a survey of CAC 40’s companies.

However, the average salary of a data analyst depends strongly on their specialization: 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.

How to pursue a career as a Data Scientist or a Data Analyst?

The profession of a data analyst is easier to learn than data scientist’s courses. DataScientest, 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. Upon successful completion of your training, you will receive a certified degree from The 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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