Although the necessary knowledge and tools used by both are very similar (statistics, mathematics, programming and business knowledge), they are not the same thing.
Data analysis is to process the data to perform analyses (usually statistics) to validate hypotheses, draw conclusions and help in decision making. Generally speaking, the steps to make an analysis are:
- Understanding which question is to be answered by the analysis, that is, what one wants to know with that analysis.
- Understand your data and establish your premises.
- Hypothesize.
- Validate the hypotheses raised
- Draw conclusions
An example is analyzing the sale of a product and seeing which type of marketing approach is most efficient.
Data science "aims to extract knowledge, detect patterns and/or gain insights for possible decision-making". To do this, capturing, storing and processing (whether by analyzing or developing models) data is possible steps of a project. That is, data science encompasses data analysis.
Bibliography:
https://en.wikipedia.org/wiki/Data_analysis
https://en.wikipedia.org/wiki/Data_science
http://datascienceacademy.com.br/blog/qual-a-diferenca-entre-o-analista-de-bi-e-o-cientista-de-dados/
https://fia.com.br/blog/ciencia-de-dados-data-science/
https://www.fm2s.com.br/analise-de-dados-como-estruturar/