There are numerous tools available for data analytics, and the choice of tools often depends on specific requirements and preferences. However, here are five popular tools commonly used for data analytics:

Python: Python is a versatile programming language that offers a wide range of libraries and frameworks for data analytics, such as Pandas, NumPy, and scikit-learn. It provides robust data manipulation and analysis capabilities and is widely used in the data science community.

R: R is a programming language specifically designed for statistical computing and graphics. It has a vast ecosystem of packages that support various statistical techniques and visualizations. R is commonly used for data exploration, statistical modeling, and creating data visualizations.

Tableau: Tableau is a powerful data visualization tool that enables users to create interactive and visually appealing dashboards and reports. It supports connecting to various data sources, including databases and spreadsheets, and provides drag-and-drop functionality for creating visualizations.

SQL: SQL (Structured Query Language) is a standard language for managing and manipulating relational databases. It is used to extract, transform, and analyze data stored in databases. SQL allows you to perform complex queries, aggregate data, and join tables to derive insights from large datasets.

Apache Hadoop: Apache Hadoop is an open-source framework that facilitates the distributed processing of large datasets across clusters of computers. It provides scalable storage and processing capabilities, making it suitable for big data analytics. Hadoop includes components like Hadoop Distributed File System (HDFS) for storage and MapReduce for processing.

Please note that the selection of tools can vary based on the specific requirements and the nature of the data analysis project.