A data analyst and a data scientist are both responsible for analyzing data and using it to inform decision-making, but they have some key differences in their skill sets and responsibilities.

Data analysts tend to focus on more specific aspects of data analysis, such as working with large sets of structured data, cleaning and organizing data, and using statistical techniques to identify trends and patterns. They may also be responsible for creating visualizations and reports to communicate their findings to stakeholders.

Data scientists, on the other hand, have a broader set of skills that includes everything that a data analyst does, as well as the ability to work with unstructured data, use machine learning algorithms to make predictions and solve complex problems, and communicate their findings to both technical and non-technical audiences. They may also be responsible for developing and testing prototypes for new data-driven products and services.

In general, data analysts are more specialized in their skills and focus on the more technical aspects of data analysis, while data scientists have a more holistic understanding of how to use data to solve business problems and drive innovation.

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What are the tools needed to become a data analyst?
Knowledge of Microsoft Excel, SQL, PowerBI, Python and its libraries such as Numpy, Pandas, Matplotlib and seaborn.

What are the tools needed to become a data scientist?
In addition to the tools used as a data analyst, you need knowledge of Machine learning models such as linear regression, logistic regression, decision tree, random forest, support vector machine, KNN, and K-means

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