Data Science and Data Analytics are two terms that are often used for the same purpose.
It is important to know that they both have different definitions.
Both fields work with data and share the same goal: to translate data analysis into business intelligence.
The main difference between data analytics and data science is in what data analysts and data scientists do with data, meaning the tactics used. Here is how they are differentiated:
Data scientists design and create new processes for data modeling. They use algorithms, predictive analytics, and statistical analysis. Data scientists have the technical skills to arrange unstructured data and build their methodologies to make predictions based on data trends. Whereas, Data analysts collect and examine large data sets to identify trends, forecasts, and data visualizations to tell a compelling story through actionable insights. These insights help businesses make informed decisions about business needs.
This diagram illustrates the differences between data analytics, and data science, and what they have in common.
This small blog will give you a surface idea of what data science and data analytics are.
source: IBM data fundamentals.