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Popular tools for data analytics in C++
Data analytics is the processing of data to extract useful information that supports the machine to make decisions. The processing of data involves cleaning, remodeling, and inspecting data.
Data analytics requires high computing power as the data to process is too large. So there are specialized tools for data analytics.
Some popular data analytics tools are −
R is one of the best and most widely used tools for data analytics available for all major platforms like Windows, macOS, Unix. It has found usage in data modeling and statistics. Easily manipulation and representation of large data are done using R as it has huge library support for data analytics. 11,556 packages are available in R that make the job of the data scientist easy.
Another programming language in the list, python is a multipurpose and most versatile programming language. This is widely used because of its large library and easy to understand nature. It is common amongst a user who needs a tool with features of both machine learning and data analytics as it has a huge set of packages backing both.
A free data visualization tool that creates visualizations, maps, and dashboards, etc. It can easily connect to data sources to extract data for visualization and also supports the sharing of visualizations to the client or on social media. It has the capability to process big data and can visualize data in a better way.
Sas is a programming language cum environment that is used for data manipulation. It is used to analyze large sets of data and manage them. It is an efficient Social media marketing tool.
It is a simple and basic tool that can be used for analytics. Data scientists use this as a first-level tool. It is an important tool previewing data sets and adding filters to data. It has much-advanced business analytics to help users in modeling.
Apache Spark Apache spark is a scalable data processing tool that is used to work with Hadoop data clusters. It is a tool that helps data science and used for machine learning model development as it supports techniques like classification, regression, clustering, and filtering to help to learn from data sets.
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