Syntax of Python Language Discussion Paper 1

December 15, 2022

Syntax of Python Language Discussion Paper

Order Number	636738393092
Type of Project	ESSAY
Writer Level	PHD VERIFIED
Format	APA
Academic Sources	10
Page Count	3-12 PAGES

Instructions/Descriptions

Syntax of Python Language Discussion Paper

Discussion-1

[Order Now]

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

[Order Now]

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

Syntax of Python Language Discussion Paper

RUBRIC

Excellent Quality

95-100%

Introduction

45-41 points

The context and relevance of the issue, as well as a clear description of the study aim, are presented. The history of searches is discussed.

Literature Support

91-84 points

The context and relevance of the issue, as well as a clear description of the study aim, are presented. The history of searches is discussed.

Methodology

58-53 points

With titles for each slide as well as bulleted sections to group relevant information as required, the content is well-organized. Excellent use of typeface, color, images, effects, and so on to improve readability and presenting content. The minimum length criterion of 10 slides/pages is reached.

Average Score

50-85%

40-38 points

More depth/information is required for the context and importance, otherwise the study detail will be unclear. There is no search history information supplied.

83-76 points

There is a review of important theoretical literature, however there is limited integration of research into problem-related ideas. The review is just partly focused and arranged. There is research that both supports and opposes. A summary of the material given is provided. The conclusion may or may not include a biblical integration.

52-49 points

The content is somewhat ordered, but there is no discernible organization. The use of typeface, color, graphics, effects, and so on may sometimes distract from the presenting substance. It is possible that the length criteria will not be reached.

Poor Quality

0-45%

37-1 points

The context and/or importance are lacking. There is no search history information supplied.

75-1 points

There has been an examination of relevant theoretical literature, but still no research concerning problem-related concepts has been synthesized. The review is just somewhat focused and organized. The provided overview of content does not include any supporting or opposing research. The conclusion has no scriptural references.

48-1 points

There is no logical or apparent organizational structure. There is no discernible logical sequence. The use of typeface, color, graphics, effects, and so on often detracts from the presenting substance. It is possible that the length criteria will not be reached.