In the era of Big Data, companies require experts who can decipher data. This is precisely the role of the data analyst and the data scientist. However, while these two professionals excel in data analysis, their roles differ within organizations. So, what are the similarities? What are the differences? Explore the data scientist vs data analyst matchup.
Data analyst vs Data Scientist: Who does what?
Before delving into the differences and similarities between a Data Analyst and a Data Scientist, it’s important to understand the role of each.
Data analyst vs Data Scientist - The role of the Data Scientist
The Data Scientist is the expert in the field of data science. They are responsible for modeling massive volumes of data to facilitate decision-making.
To fulfill this mission, the Data Scientist creates algorithms, automation systems, and data frameworks.
This work enables them to perform predictive analyses based on data and solve complex problems. The Data Scientist’s role is at a macro level, addressing the most critical questions within the organization.
Data analyst vs Data Scientist - The role of the Data Analyst
As the name suggests, the Data Analyst’s role is to analyze data. To do this, they gather data to identify trends and assist organizations in their decision-making processes.
To accomplish their mission, the Data Analyst primarily relies on statistics. These statistics enable them to address specific business questions and solve problems.
They must also ensure they use relevant, reliable, and high-quality databases for effective analysis.
Therefore, the processing of the company’s data is crucial. The Data Analyst needs to clean the data, meaning they transform the information into the correct format, eliminate unnecessary data, or find missing data.
What are the similarities between Data Analyst vs Data Scientist?
The terms Data Analyst vs Data Scientist are often confused, and for a good reason: both professionals operate in the Big Data environment.
As we live in the information age, these two professions are particularly popular in the job market.
Accessibility, understanding, use, and visualization of data provide organizations with a competitive advantage. Whether it’s through better customer knowledge, improved supply chain management, or cost control, organizations increasingly need data experts—Data Analysts and Data Scientists. These experts translate vast amounts of raw data into tools for strategic decision-making.
This ability to harness digital data is reflected in similar skill sets.
Data Analysts and Data Scientists must master mathematics, statistics, or computer science. They have typically completed at least an undergraduate degree in one of these fields.
Furthermore, they must have an excellent understanding of business to provide relevant data analysis to decision-makers, not to mention strong communication skills.
Finally, Data Analysts vs Data Scientists often work with multidisciplinary teams based on the organization’s goals. They collaborate with other experts from the IT department and also with business experts (digital marketing, product, finance, etc.) to provide analyses that closely align with the organization’s needs.
Despite all these similarities, Data Scientists and Data Analysts encompass two distinct professions.
What are the differences between Data Scientist and Data Analyst?
Tasks
While both Data Analysts and Data Scientists aim to extract insights from data, their daily missions differ.
Data Analysts are primarily focused on regular analysis and reporting. On the other hand, Data Scientists are responsible for defining predictive models that are later used by Data Analysts.
💡In summary, the key difference is that Data Analysts deduce trends from existing data, while Data Scientists build new models to enhance predictive data analysis.💡
It’s worth noting that the tasks of these professionals can also vary significantly depending on the industry or type of organization they work in.
Skills
Given the differences in the missions of Data analyst vs Data Scientists, they also possess distinct skill sets.
This is reflected in the software tools they use. Data Analysts primarily use SQL tools to query relational databases. They may also use programming languages like R or SAS, as well as visualization tools such as Power BI or Excel.
On the other hand, Data Scientists rely more on Python, Java, Machine Learning, and Artificial Intelligence (AI) to extract insights from data. They also work with technologies like Hadoop and Spark to design Big Data architectures.
In addition to technical skills, Data Scientists and Data Analysts also differ in their soft skills.
Data Scientists need to demonstrate a deeper understanding of business, a strong sense of critical thinking, and a genuine spirit of innovation to create data models capable of solving complex problems.
Training
Data Science is often more complex than analytics, which is why Data Scientists typically have a higher level of education. A significant majority of Data Scientists (94%) hold a master’s degree in science or mathematics, whereas Data Analysts more frequently have a bachelor’s degree (76%) in business or science.
In both cases, it’s possible to pursue specialized training to further enhance their skills.
Career
Early in their careers, Data Analysts primarily focus on creating reports and dashboards. Over time, their role becomes increasingly strategic.
In terms of future prospects, many Data Analysts move toward management roles where they oversee the work of other Data Analysts. In some cases, they may also undergo specific training to transition into the role of a Data Scientist.
Salary: Data analyst vs Data Scientist
Unsurprisingly, Data Scientists generally earn a higher salary than Data Analysts. This can be attributed to the increased complexity of their profession and their typically more advanced level of education.
In the United States, for example, a Data Scientist can earn an average of around $100,000 per year, while a Data Analyst may have an annual salary of around $60,000.
Please note that these figures are in the context of the United States. In France, both professionals can earn between €35,000 and €60,000 per year. However, according to our research, the lack of salary difference in Europe is often because Data Analysts and Data Scientists are still frequently confused. To the extent that often, Data Analysts actually perform the tasks of Data Scientists.
In any case, salaries vary significantly based on the industry, experience, and the specific company they work for.
