Both are not the same. Data analysts try to find trends and information from the data. In contrast, Data Scientists work on finding trends and predicting new data from that data by making models.
In the real world, the data is not always purely clean. The data may be incomplete, inaccurate, duplicated, or inconsistent data. As Data Scientist you must be able to clean the data.
Everyone can learn and become a data scientist. You don’t need to have a specific Engineering or Ph.D. degree. You need to have the skills required to become Data Scientist.
Data Science Competition(like Kaggle) has limited data, evaluation rules, and no need to rewrite code after a certain time. But real-world projects are exactly the opposite of these.
Adding more data may be required to reconstruct the model and also increases noise in the data sometimes. So more data may or may not improve the model’s performance.
Data Science does not mean only Data scientists. Data Science includes Data Scientists, Data Engineers, and Data analysts.
Data science has nothing to do with small or big organizations. Data is everywhere, thus data science helps every organization.
Communication skill is required to coordinate with team members as well as to present final results. thus this skill is necessary for Data Science.
Companies required a person who can solve real-world problems with Data Science. Therefore, make projects and showcase your skills.