There are many applications of Python across various platforms. It is a high-level, object-oriented programming language. Most programming prefer to learn Python as their first programming language due to its ease and versatility. Pandas is one of the widely used data analytics library that comes with Python. Python is a free, open-source software that can be used for a high level of visualisation and comes with packages such as Matplotlib, Seaborn. It comes with a wide set of package/libraries. Python is one of the most powerful Data Analytics tools that is available to the user. The R programming language was named after the two founders, both of whose names start with the letter R.Ī few of the multinational companies such as Google, ANZ, and Firefox are making use of R as their programming language. It is currently developed by the R Development Core Team. R is free to download from it’s official website.Ĭreated by Ross Ihaka and Robert Gentleman at the University of Auckland, R is freely available under the GNU General Public License. And it also has a huge community of developers for support. With the help of R, it is easy to perform data manipulation with packages such as plyr, dplyr, and tidy. It is excellent when it comes to data visualization and analysis with packages such as ggplot2, lattice, plotly etc. It is an open-source programming language. R is one of the most popular languages for statistical modelling, visualization, and data analysis. The data analytics tools listed below are in no particular order. How do we decide which data analytics tool to choose? This blog talks about the top data analysis tools that one can use to increase efficiency. A data analyst’s key responsibility is to gather insights from data and to do so, they must make use of the various data analytics tools available to them. The information could be vital in making data-driven business decisions. To solve the purpose of converting or transforming raw data into valuable information and to work on data analysis projects, there exist multiple Data Analytics tools in today’s world. It is everywhere, Data analytics in Finance, eCommerce, Media, and what not. Data Analysts are making use of this data on a regular basis and thus the advent of Data Analytics. This data is extremely helpful in making important business decisions, gathering insights, and providing data-based solutions. The data being generated on a daily basis has increased massively over the years, with close to 2.5 quintillion bytes generated on a daily basis.
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