In this article series “Data Scientist vs. Data Analyst”, we compare both professions and clarify whether you are better suited as a Data Analyst or Data Scientist. We take a look at the differences and similarities, career paths, tasks, essential skills and the salary as a Data Analyst and Data Scientist.
The salary of Data Analysts and Data Scientists
While the salary should not be the decisive point for a further education or a career change, you are still sure to want to know what earning potential Data Scientist and Data Analyst have to offer.
Depending on your experience level and the skills will you bring to your job as a Data Analyst or Data Scientist, your salary will also increase.
How much does a Data Analyst earn? Entry-level Data Analysts can expect a high annual salary of around € 50,000. With more experience as a Data Analyst, your salary will rise accordingly to around € 60,000, and highly experienced Data Analysts can earn up to € 75,000 a year.
How much does a Data Scientist earn? A Data Scientist salary is slightly higher than a Data Analyst salary, because they require more skills, especially in the field of programming. The salary information below is more of an average and can increase significantly if you bring more specialized skills to the table as a Data Scientist and continue to learn on the job.
Entry-level Data Scientists earn around € 55,000 per year. Data Scientists with greater professional experience earn around € 65,000, and those who have been with the company for longer and have the right skills earn up to € 85,000 per year.
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 in the following section, we’ll look at the prerequisites and skills for both data jobs.
Prerequisites for Data Scientists and Data Analysts
Let’s first look at the academic prerequisites for the Data Analyst or Data Scientist role. Even though the roles of Data Analysts and Data Scientists are both highly demanding, they are still learnable and come with clear prerequisites just like any other tech job. Let’s take a closer look at these.
For both career paths, at least a Bachelor’s degree with a scientific focus such as mathematics, computer science or statistics is considered a good prerequisite. Especially as a prerequisite for Data Scientists, the higher the degree in a scientific field or even a PhD, the easier it is to get a job as a Data Scientist. However, this is not a fixed prerequisite.
For Data Analysts, a Bachelor’s degree in a non-technical subject such as business administration and professional experience in the field in which you would like to work as a Data Analyst are usually sufficient. A good knowledge of the industry is the trump card here.
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. The following 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 fulfil this role. If you’re now 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 here, you’ll be focusing on programs that do the math for you. However, you need to know which graph best describes your data and should be able to verify that your analyses are coherent and correct.
As a Data Scientist, you need to be able to write machine learning algorithms, which requires a highly advanced understanding of math and statistics. For this, there are specialized bootcamps where you learn these skills not just theoretically, but through learning-by-doing, which is especially beneficial to those who wouldn’t consider themselves math masterminds.
Both Data Analysts and Data Scientists need to know programming. The most important skill here 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 recommend this article.
Other important programming skills you need as a Data Scientist or Data Analyst include SQL, OOP (Object-Oriented Programming) or R.
These vary not only for Data Analyst or Data Scientist, but also depending on the company and the role. Nevertheless, the most important software types are listed here:
- Microsoft Excel
- Business Intelligence-Software
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 finer points yourself, then you’re in the right place.
Communication skills are also essential. As a Data Scientist or Data Analyst, you will be in constant contact with decision-makers from specialist departments and management. It is not only important to network well, but you must be able to adapt to any 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 questions, but approach them with an open mind. It is important here that you are always willing to continue your education, to learn new things and to enjoy acquiring knowledge.
Job entry as Data Analyst or Data Scientist without previous knowledge
Data Scientist Online Masters, Data Science Bootcamps, and other educational pathways. The options are numerous, but not equally suitable for everyone.
But let’s start with the good news: yes, you can learn both professions in just a few months and make the lateral move. You can learn a lot yourself and rely on reference books and online tutorials. But what you really need, if you want to make it through the application process as a Data Analyst or Data Scientist, is a certificate together with practical knowledge relevant to the company.
It’s not for nothing that there are so many data science bootcamps, online learning platforms and data science learning providers. You should definitely pay attention to several quality criteria here. But first, you need to clarify the following questions for yourself in order to find the right one for you from the large selection:
- Do you have previous experience with programming, mathematics or statistics?
- Are you well organized and able to easily motivate yourself to study every day for a long time?
- Do you find it easy to understand and implement technical concepts without outside, individual or even personal guidance?
If your answers to all these questions are yes, then you can try Data Science online tutorials and free Data Science bootcamps, which require some prior knowledge, a lot of self-discipline, and a quick grasp of the facts.
If you couldn’t answer each of these questions with a confident yes, you’re better off basing your potential lateral entry as a Data Analyst or Data Scientist on a solid foundation of knowledge and certification. Why? While it’s appealing to take advantage of the free Data Science learning opportunities, this field is too complex to develop at a level that leads to employment without guidance and end-to-end mentoring from experienced Data Scientists.
Even with reputable data science bootcamps, there are big differences that should play a role in your choice. It should be said that many and especially free data science bootcamps do not necessarily offer the quality and depth of information that is so crucial for beginners. It is therefore worthwhile to invest in a solid and recognized basic course that enables you to expand your knowledge autodidactically if necessary.
With a good knowledge base, which StackFuel and other certified Data Science Bootcamps can provide and also certify, you can then even start directly as a (Junior) Data Analyst or Data Scientist at companies and take your first career steps. That’s why it’s so important to choose a bootcamp that works with realistic data sets and use cases from the business world and reliably teaches you the skills you require as a career changer.
The community and mentoring of data science bootcamps are also important, as they can help you to establish important contacts early on, which can later lead to recommendations for your entry-level job as a Data Scientist or Data Analyst.
Let’s summarize the most important quality criteria for bootcamps and other learning opportunities once again:
- Real business cases and practical assignments that prepare you for everyday work
- The perfect mix of theoretical instruction, interactive learning-by-doing, face-to-face webinars, and interaction with the learning community
- End-to-end mentoring from experienced Data Scientists including onboarding and offboarding, project mentoring, weekly webinars and support
- A recognized certificate of completion – the golden ticket to your first job as a Data Scientist or Data Analyst
- Multiple language options
- Funding opportunities, because as a job seeker but also as an employee, you can be entitled to 100% cost reimbursement with an education voucher. Pay attention to the AZAV certification of the learning provider!
- And last but not least, that the provider itself is certified and therefore also recognized (for example, StackFuel is a TÜV and AZAV certified programming and data science learning provider).
Summary at a glance
We have collected the most important points of the “Data Analyst or Data Scientist” comparison for you in a free infographic.
StackFuels Bootcamp 2023: Become a Data Analyst or Data Scientist in just a few months
If you are still looking for a bootcamp with Data Science online course including certificate, then we have just the thing for you: in StackFuel’s English and German-language Data Science Bootcamp with Certificate and Data Analyst Bootcamp with Certificate, you can learn the basics and advanced skills you need for your day-to-day job as a Data Analyst or Data Scientist and gain experience in..:
- Implement complex data analytics in the domain of expertise
- Bring together data sources (databases, APIs, web crawling)
- Understanding the steps within a complex analysis
- Best practices in implementing data analytics
- Increasing competitiveness with data-driven decisions
- 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 domain
- 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
Our recognized certificate of completion, which you will receive upon successful completion of our bootcamps, is especially valuable for career changers because it certifies and confirms your new data science skills.
Find out about start dates, duration, prices and requirements on our bootcamp pages:
Have you missed Part 1 of the Data Analyst or Data Scientist article series? Click here.
- World Economic Forum (2020): The Future of Jobs Report 2022 [05.04.2022]
- Stack Exchange (2021): Data science without knowledge of a specific topic, is it worth pursuing as a career? [07.04.2022]
- Glassdoor (2022): Gehalt für Data Analyst, Munich, Germany [01.04.2022]
- Glassdoor (2022): Gehalt für Data Scientist, Munich, Germany [01.04.2022]