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In this series of articles "Data Scientist vs. Data Analyst", we compare both job descriptions and clarify whether you are better suited as a Data Analyst or Data Scientist. We will take a look at the differences and similarities, career paths, tasks, essential skills and the salary as a Data Analyst and Data Scientist.
By the way: If you are interested in further training to become a data scientist or data analyst, you can do this thanks to education voucher even free of charge under certain conditions.
Data Analyst salary vs Data Scientist salary
Even if salary is not the deciding factor for further training or a career change, you would still like to know what earning opportunities Data Analyst and Data Scientist currently offer.
| Profession | Salary (2025, typical) |
|---|---|
| Data Analyst | 53.000 € - 70.000 € (Kununu, 2025) |
| Data Scientist | 62.000 € - 87.000 € (Kununu, 2025) |
| Sources: Kununu Germany, as of 2025. | |
Top salaries can be even higher in individual cases. These values provide a practical orientation for the current salary landscape in Germany and reflect average and median values.
If you've read this far, you probably already know that the higher salary of Data Scientists is due to the fact that they need to have more specialized skills for their job. That's why we'll look at the requirements and skills for both data jobs in the following section.
Requirements for Data Scientists and Data Analysts
Let's first look at the academic requirements for the job of data analyst or data scientist. Even though both roles are demanding, they can still be learned.
- A Bachelor's degree with a scientific focus (e.g. mathematics, computer science, statistics) can provide a very solid foundation for both career paths.
- For data scientists in particular, a higher degree (master's, possibly doctorate) can be an advantage, but is not mandatory.
- For data analysts, a bachelor's degree in a non-technical subject (e.g. business administration) is often sufficient, especially if professional or industry-specific knowledge is already available.
- Strong industry knowledge can be a real advantage and career changers with experience in a specialist area have good opportunities.
By now you know that Data Scientists and Data Analysts both work with data, but each in a different way. This also requires a different set of skills and tools. These skills are required for both professions:
Mathematics and statistics
Clearly, whether you're a data analyst or data scientist, you need math and statistics to do your job. If you are thinking back to difficult math exams from high school, don't worry, a data job is not a math exam.
In fact, as a data analyst or data scientist, you will focus on programs that do the math for you. However, you need to know which diagram best describes your data and you should be able to check whether your analyses are coherent and correct.
As a data scientist you must Algorithms for machine learning which requires a very advanced understanding of mathematics and statistics.
Programming
Both data analysts and data scientists need to know how to program. The most important skill is certainly Python, a particularly versatile programming language that is important for automating processes, for example. If you want to know why learning Python is not only worthwhile, but also fun, we can help you with this article recommend
Other important programming skills you'll need as a Data Scientist or Data Analyst are SQL, OOP (Object-Oriented Programming), or R.
Software expertise
These differ not only for Data Analyst or Data Scientist, but also depending on the company and the area of responsibility. Nevertheless, we have listed the most important software types here:
- Excel
- MySQL
- TensorFlow
- Spark
- Business Intelligence Software
- SAS
- Hadoop
Soft Skills
Data Analysts and Data Scientists are characterized not only by technical skills, but also by certain soft skills. Most important is a logical, analytical way of thinking. If you enjoy finding answers to questions and questioning the questions yourself, then you're in the right place.
Communication skills are also indispensable. As a Data Scientist or Data Analyst, you are in constant contact with decision-makers from specialist departments and the management level. It is not only important to network well, but you must be able to adapt to every level of knowledge in order to present the results of your analyses in such a way that everyone understands them.
You should not immediately shy away from new, complex projects and issues, but approach them with curiosity. For this, it is important that you are always willing to continue your education, learn new things and enjoy acquiring knowledge.
Job entry as Data Analyst or Data Scientist without previous knowledge
A lateral entry into data analytics or data science is possible - often in just a few months. You can learn a lot yourself, e.g. via specialist books, online tutorials or bootcamps. However, practical knowledge and a recognized certificate that proves your skills for companies are crucial for a successful start.
You should ask yourself the following questions before choosing a training program:
- Do you have basic knowledge of programming, mathematics or statistics?
- Can you learn independently and in an organized way?
- Do you find it easy to understand and apply technical concepts independently?
If you are not yet sure about this, we recommend structured training with practical exercises and mentoring. Even renowned providers differ greatly in terms of quality and depth.
Important criteria for good training:
- Real business cases and practice-oriented tasks
- Combination of theory, learning-by-doing, webinars and community exchange
- Continuous mentoring by experienced data scientists
- Recognized certificate of completion as a ticket to a job
- Several language options and funding opportunities (e.g. education voucher, AZAV certification)
With solid training and the right practical projects, you can start directly as a (junior) data analyst or data scientist. Providers such as StackFuel offer certified programs with realistic data sets and extensive mentoring.
Certified further training: Become a data analyst or data scientist in just a few months
If you are still looking for a bootcamp with a data science online course including a certificate, then we have just the right thing for you: In StackFuel's Data Analyst and Data Scientist training course, you will learn the basics and advanced skills you need for your day-to-day work as a data analyst or data scientist and gain experience in:
Data Analyst Further Training
- Implement complex data analysis in the subject domain
- merge data sources (databases, APIs, web crawling)
- The understanding of the work steps within a complex analysis
- Best practices in the implementation of data analysis
- Increase competitiveness with data-driven decisions
Data Scientist Further Training
- Enable data-based (automated) decision making
- Implement relevant data science projects with the help of knowledge from the subject domain
- Make data-based predictions in the field
- Apply performance metrics and models of supervised and unsupervised learning with sklearn
- Know the basics of data storytelling
- Best practices of interpreting supervised and unsupervised learning algorithms such as decision trees and random forests.
Certificate
Particularly for career changers our recognized Certificate of completionwhich you receive upon successful completion of our bootcamps, is so valuable because it certifies and thus validates your new Data Science skills.
We focus on real-life practical projects and a practical approach. good mentoring by professionally experienced Data Scientists. In the Live webinars you can ask your questions and benefit from the StackFuel Community.
Find out about start dates, duration, prices and requirements here:
Conclusion
- The salary for data analysts and data scientists in Germany in 2025 is attractive and varies greatly depending on experience, specialization and role.
- Career changers have good opportunities, especially with structured learning (boot camps, certificates).
- In comparison, the role of data scientist often requires deeper technical and statistical skills, but also offers higher earning potential.
- Investing in further training can be worthwhile, as you will acquire practical skills, build up a portfolio and receive a certificate of completion that will impress employers.
