Tools For Data Science

Popular programming languages are used by data scientists to do statistical regression and exploratory data analysis. These open-source tools include pre-built machine learning, graphics, and statistical modelling capabilities. You can learn more about these languages in “Python vs. R: What’s the Difference?”

The following are some of them: R Studio:

A free and open-source environment and programming language for creating statistical computing and visuals.

This programming language is dynamic and adaptable. For rapid data analysis, the Python language comes with several libraries, including NumPy, Pandas, and Matplotlib.

Data scientists can use GitHub and Jupyter Notebooks to make it easier to share code and other information.

A user interface may be preferred by certain data scientists, and two popular enterprise tools for statistical analysis are:

SAS:

A complete set of tools for analysis, reporting, data mining, and predictive modeling that includes interactive dashboards and visualizations.

IBM SPSS:

Advanced statistical analysis, a sizable collection of machine learning algorithms, text analysis, open source extensibility, big data integration, and simple application setup are all features of IBM SPSS.

Additionally, big data processing platforms like Apache Spark, Apache Hadoop, and NoSQL databases are mastered by data scientists. They are also proficient with a variety of data visualization tools, including open-source tools like D3.js (a JavaScript library for creating interactive data visualizations) and RAW