Here are the top 10 data science tools that are used by professionals:

1. Python is a general-purpose programming language that is popular for data science. It is easy to learn and has a large library of packages for data analysis and machine learning.
2. R is a statistical programming language that is also popular for data science. It is known for its statistical functions and graphics capabilities.
3. SQL (Structured Query Language) is a database language that is used to manage data in relational databases. It is a fundamental tool for data science, as most data is stored in relational databases.
4. Tableau is a data visualization software that is used to create interactive dashboards and reports. It is a powerful tool for communicating data insights to stakeholders.
5. Power BI is another data visualization software that is similar to Tableau. It is a Microsoft product and is often used in conjunction with Microsoft Excel and other Microsoft products.
6. SAS (Statistical Analysis System) is a statistical software package that is used for data analysis and machine learning. It is a popular choice for businesses and government agencies.
7. MATLAB is a mathematical software package that is used for data analysis, scientific computing, and machine learning. It is a popular choice for engineers and scientists.
8. TensorFlow is an open-source software library for machine learning. It is used for deep learning, a type of machine learning that is used for tasks such as image recognition and natural language processing.
9. PyTorch is another open-source software library for machine learning. It is similar to TensorFlow, but it is designed for Python.
10. Apache Spark is an open-source distributed computing framework. It is used for large-scale data processing and machine learning.

These are just a few of the many data science tools that are available. The best tool for you will depend on your specific needs and preferences.

Here are some additional tips for choosing data science tools:

• Consider your needs. What tasks do you need to perform? What data do you need to work with?
• Consider your skills. What programming languages do you know? What statistical software packages are you familiar with?
• Consider your budget. Some data science tools are free or open-source, while others are commercial products.
• Consider the community. What is the size and activity of the community of users for the tool? This can be a valuable resource for learning how to use the tool and for getting help with problems.

By following these tips, you can choose the right data science tools for your needs.