Which software is commonly used for statistical analysis in data analytics?

Statistical analysis is crucial in data analytics, and the most commonly used software tools for this purpose are R and Python. Both R and Python are programming languages equipped with extensive libraries and frameworks that enable complex statistical operations and data manipulation.

R was specifically designed for statistics and data analysis, making it particularly powerful for statistical operations, data visualization, and working with large datasets. It includes numerous statistical packages and functions that simplify the analysis process for users.

Python is also prevalent in the data analysis field due to its versatility and user-friendly syntax. Libraries such as Pandas, NumPy, and SciPy along with statistical packages like Statsmodels make Python a potent tool for both data analytics and statistical analysis. This adaptability allows data analysts to carry out a wide range of analyses from simple descriptive statistics to complex predictive modeling.

Other options do not serve the same purpose: for instance, Excel, while useful for basic data manipulation and simple statistical analysis, lacks the depth of statistical capabilities that specialized software provides. Java, primarily a programming language for application development, is not typically used for statistical analysis. Photoshop is a graphic design software with no functionalities suited for statistical computation or analysis.