Common tools and software used in data analytics include:

1. Excel: Widely used for basic data manipulation and analysis.
2. SQL: Essential for querying and managing databases.
3. Python: Popular for its libraries like Pandas, NumPy, and Matplotlib for data analysis and visualization.
4. R: A language specifically designed for statistical analysis and data visualization.
5. Tableau: A powerful data visualization tool.
6. Power BI: Microsoft's business analytics service for visualizing data.
7. Apache Hadoop: Used for processing large data sets.
8. SAS: A software suite for advanced analytics and predictive modeling.

These tools help in processing, analyzing, and visualizing data efficiently.
 

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Common Tools and Software Used in Data Analytics

1. Microsoft Excel: Versatile for data manipulation, basic analysis, and visualization.
2. Google Sheets: Cloud-based tool for collaboration and simple data analysis.
3. Tableau: Powerful for creating interactive and shareable dashboards.
4. Power BI: Microsoft’s tool for business intelligence and custom visualizations.
5. SQL: Language for managing and querying relational databases.
6. Python: Programming language with libraries like Pandas and Matplotlib for data analysis.
7. R: Specialized in statistical computing and data visualization.
8. SAS: Suite for advanced analytics and predictive modeling.
9. Apache Hadoop: Framework for processing large data sets across distributed systems.
10. Apache Spark: Fast data processing engine for big data analytics.
11. Jupyter Notebooks: Interactive environment for coding, visualization, and documentation.
12. Looker: Business intelligence tool for data exploration and custom dashboards.

These tools help in analyzing, visualizing, and managing data effectively.

Here are some tools and software names that are use in data analytics. 

1. Excel: Widely used for basic data analysis and visualization.
2. R: A statistical programming language for advanced analysis and visualization.
3. Python: Offers powerful libraries like Pandas, NumPy, and Matplotlib for data manipulation and analysis.
4. SQL: Essential for querying relational databases.
5. Tableau: Popular for interactive data visualization and dashboard creation.
6. Power BI: A Microsoft tool for business analytics and data visualization.
7. SAS: Used for advanced analytics and statistical analysis.
8. Apache Spark: Handles large-scale data processing and analytics.

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In data analytics, I rely heavily on tools like Python for its flexibility and libraries, mern stack training institutes in kochi Excel for quick data manipulation, and Tableau for its powerful visualization capabilities. Each tool has its strengths, making them essential for analyzing data effectively and deriving actionable insights across various projects.