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Discussion-1: Most of the data analytics and statistics projects nowadays use R or Python programming languages.

Discussion-1: Most of the data analytics and statistics projects nowadays use R or Python programming languages.

Discussion-1

Most of the data analytics and statistics projects nowadays use R or Python programming languages. The language selection depends on the data and type of the analytics project

The syntax of the Python language is easy and quick to understand. Hence, programmers are more productive and efficient, and the development time is less than projects implemented in other languages(Ozgur et al., 2017). In Python, everything is considered as an object which has its namespace. This feature provides a clean and simple structure that helps with introspection (Ozgur et al., 2017).

R is built specifically for data analytics and visualization projects. It is also flexible and has several features which can be added in packages as needed. R itself keeps adding new features, and some of them are also delivered by User-created code packages. As R was built for analytics specifically, its analytical power is better than the other programming languages. R can handle large datasets and have better visualization capabilities (Ozgur et al., 2017).

In conclusion, R provides a vast number of features like visualization and handling massive datasets. However, it is a challenge to improve the performance of R when handling these large datasets. Whereas Python is easy to learn and understand language and should be a good fit in projects with less data and high performance is required.

Discussion-2

Data visualization is one of the parts of data analysis. It is the graphical representation of data so that it can provide meaningful insights to the audience. There are different ways in which the data can be converted into graphs. There are many data visualization tools such as SAP Cloud Analytics that can visualize the data and organize it into various graphs or charts. However, these tools become more powerful when they can be used with programming languages such as R and Python. Both Python and R are beneficial when it comes to data visualizations.

While Python is a general-purpose language, R is mainly based on statistics. Python is easy to learn and has a readable syntax for the users. Python can be used to carry out data analysis or use machine learning in scalable environments. It offers data visualizations with the help of different libraries such as Matplotlib and Seaborn. It would allow users to create plots with less code than that of R-language (Weintrop & Holbert, 2017).

When it comes to R, it is mainly used to create statistical models based on statistics. It would help data scientists to create plots using their default packages. Ruses ggplot2 and to creates a step-by-step procedure for data visualization. Compared to Python, R offers more default packages that could be useful for data visualizations (Lebanon & El-Geish, 2018). However, most users find it easier to work with Python as it offers more straightforward syntax than R-language. Below are example programs of R and Python, which use different functions and libraries.

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