What is the difference between Data Analyst and Data Scientist?

What is the difference between Data Analyst and Data Scientist?

Interested in Jedha's training courses?
See the Jedha syllabus
Our latest articles!

In this article, we will explain the main differences between the Data Scientist and Data Analyst jobs that are accessible with our Data Analysis and Data Science courses. It is true that the two professions may seem interchangeable or at least that the border between the two seems relatively blurred. What are the differences in terms of missions, technical skills and salary? 

What is a Data Analyst?

Even if the objective of both jobs is to manipulate and exploit data, it will not be for the same purposes. The Data Analyst will retrieve existing data, analyse it or create dashboards allowing management to steer their company with the main KPIs to make informed decisions. 

Be careful though, we often reduce the job of Data Analyst to the creation of dashboards, but this job goes much further than that. The Data Analyst is often close to the core business of the company: he must know the environment of the company, its functioning, its competitors, the tools used to meet the internal needs of analysis (analyses to be conducted for the Marketing, Finance, Controlling or other departments). 

Data Analyst vs Data Scientist
Data Analyst vs Data Scientist

What is a Data Scientist?

On the other hand, the Data Scientist is more involved in the algorithmic, programming and statistical aspects. They build statistical models from existing data in order to predict future phenomena (churn rate, fraud, sales prediction, income) using Machine Learning algorithms. The Data Analyst will position himself on descriptive statistics to get information out of his analysis from data that we already have, or by automating processes, tasks.

In a Tech company, the Data Scientist can also work directly for the product teams, in order to improve certain features, implement recommendation systems, predict the purchase of a user etc. For example, one of the main missions on which a Data Scientist can work is the prediction of the lifetime value: how much will my user bring me by subscribing to my product? 

Code: a key skill for any data professional!

The difference in terms of skills: Data Analyst vs. Data Scientist

Skills required for a Data Analyst

Although the two professions may have some skills in common on certain assignments, there are some predominant differences. For the Data Analyst, one of the main skills is SQL, a language that will allow you to query and manipulate databases. 

With the idea of reporting the main KPIs to your managers, you will have to pick up information that is ultimately found at the crossroads of several databases. As a Data Analyst, it will be your job to go and get the right information from the right places. Once this is done, you will create a dashboard, allowing you to clearly present your analysis results, in order to inform operational or strategic decisions.

Skills required for a Data Scientist

As for the Data Scientist, he will have a predominance of skills on programming languages, often Python. Why this one in particular? Thanks to this language, the Data professional will be able to create Machine Learning algorithms, put them into production, interact with large volumes of data (Big Data issues): the applications of Python are countless, which makes it an extremely sought-after skill, even among Data Analysts.

If you look at the missions on Welcome to the Jungle or on another job board, you may see an offer asking for a Data Analyst who knows how to code in Python, and a Data Scientist who knows SQL, these 2 skills are far from being the only ones in the 2 arcs.

Data visualization, database cleaning, statistics, many technical skills are attributed to these two professions. The profile most appreciated by recruiters? A curious profile, with transversal skills. There is simply a predominance in the skills to be applied for the respective missions.

The difference in salaries

What is the salary of a Data Analyst?

It all depends of course on the type and size of the company. If we put ourselves in the same organization, the Data Scientist will generally earn relatively more than a Data Analyst, especially when it is a junior profile. 

To give you an idea, the Data Analyst can earn between 35 and 40 000 € gross per year.

What is the salary of a Data Scientist?

A Data Scientist is around 38 000 - 42 000 €. Coming to senior profiles with experience, these salaries still vary a lot, again, depending on the type of mission, the type of organization, the typologies of data processed. These estimates made above are based on Glassdoor's as well as on Jedha's alumni placed in companies.


In conclusion, we often attribute a higher demand to Data Scientists, but think again! As much as on the Data Analysts side, the demand is strongly increasing, even more so with the covid crisis. The reason is simple: companies need a lot of relevant analysis, quickly, in order to make decisions on a very short term.
If you want to know more about these jobs, you can have a look at our Data Analyst job description and Data Scientist job description! If you want to acquire the Data skills that recruiters are looking for, don't hesitate to have a look at the Data trainings that Jedha Bootcamp offers.

Don't hesitate to find out about the best data science courses.

Frequently asked questions

How to go from Data Analyst to Data Scientist?

To move from Data Analyst to Data Scientist, you need to acquire technical skills in machine learning and data modeling, strengthen your knowledge of mathematics and statistics, become familiar with Python, R, and SQL, develop hands-on experience working on projects, and come up with new approaches to solve business problems.

How to become a Data Scientist without a degree?

To become a Data Scientist without a degree, self-train to gain skills in programming, statistics, and Data Science tools such as Python, R, and SQL by taking online courses and practicing on personal projects. Bootcamps and training programs can also help, but hands-on experience is essential to developing the skills and confidence needed to work as a Data Scientist.

Richard Gastard
Written by
Richard Gastard
General Manager