What Is The Difference Between A Data Analyst And A Data Scientist?
Both data analysts and data scientists work with data, but they do so in different ways.
In the World Economic Forum’s Future of Jobs Report 2020, data analysts and data scientists were listed as the most in-demand, high-paying jobs in 2021, followed immediately by AI and machine learning specialists and big data specialists
Though there’s undeniably a lot of interest in data professionals, it might not be clear what the difference is between a data analyst and a data scientist. Data analysts and data scientists are both involved with data but in different ways.
Data analysts and data scientists: What do they do?
One of the biggest differences between data analysts and scientists is what they do with data.
A data analyst works with structured data to solve tangible business problems using tools such as SQL, R, or Python programming languages, data visualization software, and statistical analysis.
Common tasks for a data analyst might include:
To deal with the unknown, data scientists often use more sophisticated data techniques to make predictions about the future. They may automate their own machine-learning programs or design predictive modeling processes that can handle structured and unstructured data, alike. This role is generally considered a more advanced version of a data analyst.
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Some day-to-day tasks might include:
Data Science vs. Analytics: Educational Requirements
As a general rule, data analyst jobs require a bachelor’s degree in a field like mathematics, statistics, computer science, or finance. Data scientists (as well as many advanced data analysts) typically hold a master’s or doctoral degree in a field like data science, information technology, mathematics, or statistics.
If you’re just starting out, working as a data analyst first can be a good way to launch a career as a data scientist.
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