Data analytics helps forecast future value whenever we want to know what will happen next. To draw conclusions from data, one uses data analytics. It often entails data collecting and examination and has one or more users.
Data analysis is performed on the historical dataset, to comprehend what has occurred thus far from the data. In short, data analysis is used to extract precise insights from past data. To get a useful outcome, it required defining the data, investigating it, cleaning it up, and modifying it.
Identifying the issue, locating the data, data filtering, data validation, data cleaning, data visualization, data analysis, conclusion, prediction, etc. are just a few of the stages that make up the data analytics process.
Similar to this, the process of data analysis includes data collection, data validation, interpretation, analysis, results, etc.; it ultimately aims to determine what the data is attempting to convey.
Python, R, Microsoft Excel, Tableau, Qlik, Qlik Sense, Power Bi, and Sisense are some of the common programs that are used for both data analytics and analysis. You must, however, be knowledgeable about databases.
In conclusion, both data analytics and data analysis are necessary for understanding the data.
Big Data & SQL 