Data Analytics

Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements. Data analytics is also known as data analysis.

Top Tools in Data Analytics- With the increasing demand for Data Analytics in the market, many tools have emerged with various functionalities for this purpose. Either open-source or user-friendly, the top tools in the data analytics market are as follows.

R Programming- R is the most popular data analytics tool as it is open-source, flexible, offers multiple packages and has a huge community. R compiles and runs on various platforms such as UNIX, Windows, and Mac OS. 

Python- Python is an open-source, object-oriented programming language which is easy to read, write and maintain. It provides various machine learning and visualization libraries such as Scikit-learn, TensorFlow, Matplotlib, Pandas, Keras etc. It also can be assembled on any platform like SQL server, a MongoDB database or JSON.

Table Public- This is a free software that connects to any data source such as Excel,  corporate Data Warehouse etc. It then creates visualizations, maps, dashboards etc with real-time updates on the web.

Microsoft Excel- This tool is one of the most widely used tools for data analytics. Mostly used for clients’ internal data, this tool analyzes the tasks that summarize the data with a preview of pivot tables.

Apache Spark- One of the largest large-scale data processing engine, this tool executes applications in Hadoop clusters 100 times faster in memory and 10 times faster on disk. This tool is also popular for data pipelines and machine learning model development.

SAS- A programming language and environment for data manipulation and analytics, this tool is easily accessible and can analyze data from different sources.

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