There have been quite a lot of queries relating to the differences between Data Science and Data Analytics.
So, here we go clearing them out!
Data scientists use programming, math, and statistics to gain insights and drive organizational strategy.
Data analytics professionals are responsible for data processing, and other techniques to gain insights from data.
Data scientists require an in-depth knowledge of programming languages like Python, Scala, R programming, and Ruby.
Data Analytics requires only basic programming language knowledge in Python, R, and proficiency in SQL.
Data Scientists model data to make predictions, identify opportunities, and support strategies.
Data analyst solves problems and spot trends.
Data scientists use machine learning to improve the ways data is utilized.
Data analysts collect, store, and maintain data to analyze trends.
While you might have got an idea about the sky and the land difference between the two, you would have also figured out that Data Science looks like a more interesting career option to choose.