Data Analyst or Data Scientist Salary—Who Earns More?

Data-related careers are among the fastest-growing worldwide. With businesses and organisations heavily reliant on data for decision-making, roles such as data scientists and data analysts are becoming crucial. And depending on their work, the Data Analyst vs Data Scientist salary differs.

These roles may sound similar, but they have distinct responsibilities and skills, as well as career pathways.

The main work of data analysts involves collecting, processing, and visualising data, as well as cleaning data and creating reports. Data analysts’ work simplifies informed decision-making. Whereas data scientists are responsible for developing algorithms, building models, and drawing insights from complex datasets.

In this article, we will understand the roles of a data analyst and a data scientist. Additionally, we will explore data analyst salary vs data scientist salary in 2026, comparing who earns more: data scientist vs data analyst according to the skills required, and data analyst vs data scientist career path.

Key Difference Between Data Scientist and Data Analyst

The primary distinction between data analysts and data scientists lies in the types of tasks they handle. Data analysts assess past and current data to support decision-making, while data scientists use advanced techniques to predict outcomes and drive innovation. 

Here’s a clear comparison of their key differences:

Aspects	Data Analyst	Data Scientist
Role	Interpreting existing data	Building predictive and advanced models
Key Tasks	Analysing and visualising trends	Developing algorithms and forecasting
Tools Used	Excel, SQL, Power BI, Tableau	Python, R, ML algorithms, Big Data tools
Business Output	Reports and dashboards	Predictive models and advanced solutions
Level of Complexity	Moderate	High

Essential Skills for Data Analysts and Data Scientists

The skill sets of Data Analysts and Data Scientists differ based on the complexity of the data problems they handle. While both roles work with data, the tools, techniques, and depth of expertise required vary significantly.

Here’s a closer look at the skills of data scientists and data analysts:

Skills for Data Analyst Role:

A Data Analyst focuses on extracting insights from existing data to support business decisions and improve performance. 

• Excel and spreadsheets for data cleaning, organisation, and analysis
• SQL for querying and managing databases
• Data visualisation tools like Power BI or Tableau
• Basic statistics and descriptive analytics
• Basic understanding of business reporting

Read More: Data Analyst Tools—Complete 2026 Guide

Skills for Data Scientist Role:

A Data Scientist uses predictive models and algorithms to work on advanced data problems and drive strategic outcomes. 

• Use of Python or R for data analysis and model development
• Implementing machine learning and statistical modelling techniques
• Data preprocessing and feature engineering
• Big data tools and cloud platforms
• Strong problem-solving, mathematical, and analytical foundations

Data Analyst vs Data Scientist Salary

Now, finally, we have the answer to this ongoing debate: who earns more, a data scientist or a data analyst? Data scientists generally earn more than Data Analysts due to their advanced technical expertise and the strategic business impact that they deliver.

As organisations invest more in AI, automation, and predictive analytics, the gap between Data scientist salary vs data analyst salary in 2026 increases, especially for experienced professionals.

In order to understand this difference properly, let’s look at how salaries differ across roles and experience levels:

• In South Africa, data analysts earn approximately R306,004 per year, depending on the level of the role.
• Data scientists earn more on average, with salaries of R574,462 per year, especially in technology-focused companies.
• At the entry level, salaries for both roles can be similar, but the gap becomes noticeable only after 3 to 5 years of experience.
• Factors such as industry, company size, and city location play a significant role in determining pay for both roles.

Read More: Data Science Careers in South Africa 2026—Salary, Demand, and Future Outlook

Data Analyst vs Data Scientist Career Path

Both data analysts and data scientists have exciting career pathways that offer multiple opportunities for growth. Choosing between these careers depends on your interests and goals.

Data analysts focus on gaining insights from the data. Whereas data scientists support advanced techniques such as machine learning and artificial intelligence, which often lead to more specialised, higher-paying roles. 

Below are common career paths for each role:

1. Data Analyst:

• Junior Data Analyst—Work with spreadsheets, SQL, and basic dashboards to clean data and generate reports.
• Data Analyst—Analyze trends, create visualisations, and support business decision-making across teams.
• Senior Data Analyst—Lead complex analysis projects, mentor juniors, and present insights to stakeholders.
• Analytics Manager—Oversee analytics teams, define reporting strategy, and align data insights with business goals.
• Transition Opportunities—With upskilling in Python, statistics, and machine learning, data analysts can move into data science roles.

2. Data Scientist:

• Junior Data Scientist/Data Science Associate—Work on data preparation, basic models, and exploratory analysis.
• Data Scientist—Build predictive models, apply machine learning algorithms, and solve complex business problems.
• Senior Data Scientist—Design advanced models, work with large datasets, and lead high-impact data projects.
• Lead Data Scientist/AI Specialist—Drive AI strategy, mentor teams, and influence product or business innovation.
• Advanced Leadership Roles—Progress into roles such as Head of Data, AI Manager, or Chief Data Officer.

Read More: Guide To Becoming A Data Scientist In South Africa

Conclusion

Understanding data has become essential as the world continues to advance in technology. And in this tech-driven world, the roles of data analyst and data scientist are crucial, as they help organisations make informed and strategic decisions.

Data scientists usually earn more than data analysts because of their advanced technical and predictive skills. However, data analyst roles remain highly valuable, especially for businesses that rely on accurate reporting and actionable insights for day-to-day decisions.

Choosing between a career path as a data analyst or a data scientist depends mostly on your interests, strengths, and long-term goals. Data analyst roles suit well for those who prefer working closely with business data and visual insights, while data scientist roles are ideal for professionals who enjoy coding, modelling, and solving complex problems using advanced analytics.

If you’re looking to build a future-ready career in data, start by choosing the path that aligns with your skills and aspirations.

Digital Regenesys provides a Data Science with AI course that can help you develop your data analysis and interpretation skills.

Visit our website, Digital Regenesys, and check out the courses in Data Science and Data Analytics to start your journey as a data scientist or a data analyst.