If you’re interested in a career in Big Data and data processing, there’s no getting around these highly sought-after roles of Data Scientists and Data Analysts.
While the hype around data experts is undeniably huge and never dying down, it’s still not clear to everyone what the difference between Data Analyst and Data Scientist is. Both work with data, but not quite in the same way.
In this series of articles, we’ll look at both professions in comparison and clarify whether you’re better suited as a Data Analyst or Data Scientist. We’ll take a look at the differences and similarities, career paths, tasks, essential skills and salary as a Data Analyst and Data Scientist.
*Disclaimer* In this article, when we refer to Data Analyst and Data Scientist, these job titles are intended to be gender neutral and refer to men, women, as well as non-binary individuals.
Why Data Analysts and Data Scientists are so popular
Data Scientists and Data Analysts are two of the most in-demand and highest-paid professions today. In fact, the World Economic Forum’s 2020 Future of Jobs Report lists both occupations as being the most in-demand occupations across all industries, closely followed by AI, machine learning and Big Data specialists, along with software developers.
The same report also found that job seekers and career changers in particular are increasingly qualifying for Data Scientist and Data Analyst jobs through online training. That’s why it’s worth taking a close look at which job really suits you, your strengths and your career goals.
Why is it worth becoming a Data Analyst or Data Scientist? Data Analyst and Data Scientist have some advantages in common. Let’s take a closer look at what you can look forward to in your job as a Data Analyst or Data Scientist:
- In both jobs, you can easily work both in the office and in the home office, all you need is a laptop and an internet connection.
- As a Data Analyst or Data Scientist, you can choose from a wide range of job offers and work either as a permanent employee or as a freelancer in almost any industry. Stepstone currently lists 10,000 vacancies for Data Analysts and 1,000 vacancies for Data Scientists. [As of 04/20/2022]
- Since both jobs bring such universal skills, they are also great complements and career boosters for most jobs. Even if you don’t want to work as a Data Analyst or Data Scientist, you can use that rare knowledge and valuable skill set to advance your career. Basic to advanced data skills are needed in almost every discipline and department, whether in marketing, HR, or finance. The demands of the job market are constantly shifting towards data-driven work. For this reason, continuing education is worthwhile in any case.
- Thanks to the increasing demand for Data Analysts and Data Scientists, you can expect an above-average salary. We will go into this in more detail later on.
- In addition, you have a high level of job security with a very promising, exciting profession that offers good career opportunities, is in demand in all industries and is crisis-proof.
- Can you become a Data Analyst or Data Scientist without studying? Yes, that is possible. Nowadays, further education is sufficient for your additional qualification. You can even train as a Data Analyst and Data Scientist online, for which there are several options – see below for further details.
If these advantages of both data professions sound motivating to you, let’s take a look at the tasks involved in being a Data Scientist and Data Analyst.
What are the duties of a Data Analyst and Data Scientist?
If we compare the profession of painter with that of baker, we can draw very clear dividing lines. If we compare the profession of Data Scientists and Data Analysts, we tend to move along a spectrum.
The fact that the two professions are not so easily distinguishable from one another is partly due to the fact that they are still comparatively young, and companies do not always draw this line clearly in their job offers because they find it difficult to distinguish between them in terms of content.
The fact that Data Analyst and Data Scientist may have similarities in their tasks and skills does not signify that they are interchangeable or synonymous by any means. In fact, it is more the case that the two professions complement each other when they work toward the same project goal. So let’s take a closer look at the areas of responsibility.
The tasks of Data Analysts
The tasks of Data Analysts are basically consulting-oriented. In their day-to-day work, data analysts are tasked with processing data from SQL databases or even Excel spreadsheets and examining them for patterns. These patterns are intended to provide management or business departments with insight to draw informed conclusions and make improved, promising business decisions.
To do so, a Data Analyst analyzes past business events, such as sales, online store registrations, or even medical diagnoses or natural phenomena. In addition, a data analyst must be able to perform clean A/B tests, where two factors are tested against each other to determine trends. With the results collected, Data Analysts can then advise business managers or product developers on which option is more likely to produce the desired result.
Why do companies need Data Analysts? Not everyone who makes important decisions in a company has the necessary skills to evaluate business data. The danger would be too great that decision-makers would rely only on their gut instinct, leaving the result to chance. They are therefore entirely dependent on the skills and advice of Data Analysts.
Although we will discuss the skills that data analysts need for their job later on, it is clear even at this stage that they must have particularly good communication skills in their consulting role.
While very few decision-makers evaluate data themselves, most can understand and act on the evaluation without a detailed explanation. This is where Data Analysts come into play again. It is their central task to visualize data, put it into a business or even industry-specific context and present it in a collected and understandable way.
It is important that Data Analysts do not falsify anything or only report what decision makers want to hear. They are like detectives or investigative reporters who make their unbiased findings available and ensure that everyone can understand them.
This makes career changers especially valuable if they have already worked in other jobs in the same company or industry and have accumulated extensive industry or process knowledge. It’s also exceedingly helpful for Data Analysts to be well-connected, as they often need to work closely with departments in order to gather information or share insights. If you understand how a department works and what processes and information are important to them, you will quickly make yourself indispensable as a Data Analyst.
The tasks of Data Scientists
While Data Analysts often have clear work assignments, Data Scientists face the great potential of the unknown. Data Scientists are therefore expected to have a deeper, and at the same time broader expertise and skills than Data Analysts. They are therefore also paid significantly more for their specialization. We will discuss the salary in more detail below.
Compared to the consulting Data Analysts, Data Scientists see themselves more as researchers and developers. Not every Data Scientist has the same tasks, but they can use algorithms to model data queries, automate processes, train artificial intelligence with machine learning and in turn, develop new business opportunities for the company.
Unlike Data Analysts, they typically use their advanced programming skills to create data pipelines that automatically transfer data from one source to another, for example. Their skills therefore enable them to use sophisticated technologies such as artificial intelligence to make even more accurate predictions, or to use machine learning to develop smart products.
Data Scientists combine several disciplines: mathematics and statistics, computer science, communication and industry knowledge. In the data industry, Data Scientists must be better at statistics than software engineers and better at programming than statisticians.
For this reason, it is clearly advantageous for Data Scientists to come from research or a quantitative or STEM (mathematics, computer science, natural science, technology) professional background and to have previous experience.
Data Analyst vs. Data Scientist – Part 2
In Part 2 of the Data Analyst and Data Scientist article series, you will find out more about the average annual salary for Data Analyst and Data Scientist, career stages, requirements for the two jobs, skills needed, and how to master the job entry process. Click here for part 2 of the series.
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, München, Deutschland [01.04.2022]
Glassdoor (2022): Gehalt für Data Scientist, München, Deutschland [01.04.2022]