Last Updated on by Kumar Raja

Data Visualization With R & Python

R and Python are considered as the state of the art in terms of programming languages that support the data analytical process in Data Science. Being open source, both Python & R are having a large community base & support which contributes to frequent updates. Both these languages are very crucial in terms of performing analytics process in Data Science so, developing skills & expertise in any of these two languages is very crucial for your Data Scientist career.

Python, a general-purpose language with easy to read syntax & is having numerous libraries that support several analytical operations in Data Science. R, which has been developed by statisticians helps to support in numerical & statistical analysis. Work towards developing hands-on skills & proficiency in working on any of the two prominent languages of Python & R with Kelly Technologies advanced Data Science Training In Hyderabad program.

In this blog post, let’s take a look at the process of Data Visualization with Python & R.

Data Visualization With Python-

Python is having a number of powerful options like IPython Notebook that helps Data Scientists Python also has numerous libraries that support Data Visualization process. With the help of Matplotlib library in Python, we can design basic charts and graphs of embedded data. Using Pythons Plot.ly, it becomes possible to design more complex graphs & visuals using which the insights from the data can be presented more accurately. 

Most of the Python APIs support the Data Scientists with user friendly dashboards using which they can present the data insights in the form of attractive visuals to pin point.

Data Visualization With R-

As we have mentioned earlier, R has been mainly designed to support numerical & statistical analysis of the data. R is having a number of special packages that help in visualizing the end results with high performance graphs. Using R, the process of Data Visualization becomes lot easier.

R has the presence of extensive libraries that support in designing complex graphs & to create plots from data matrices.  We can make use of ggplot 2 in R for handing more complex plots.

Develop complete hands-on skills in relation to working on handling analytics processes in Data Science using either Python or R by being a part of our real-time Data Science training program.

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