Order ID | 53563633773 |
Type | Essay |
Writer Level | Masters |
Style | APA |
Sources/References | 4 |
Perfect Number of Pages to Order | 5-10 Pages |
Syntax of Python Language Discussion Paper
Syntax, Python, Language, Discussion, Paper
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.
Need two replies for discussion 1 and 2 of each 150 words, no plagiarism
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