assignment 3

Discussion 1

The two languages “Python and R”, are equally popular in data visualization and analysis and competent to each other. According to the requirement, both languages play their significant role similarly in the data science problems, based on the user knowledge and capabilities on the language and resources work along. Learning or using any of these two Python and R languages will be helpful and useful in any way.

According to the author (Fahad & Yahya, 2018), Data visualization means the data representation in a graphical form, and it is easy to communicate data to the user with graphs, charts, and bars with the help of visualization techniques. Big data visualization helps the decision-maker to look into the graphical illustrations produced along. The visualization charts or graphical representation generated by the computer system allows humans to visualize a large amount of data. Big Data Visualization is a qualified and simplified process and encourages the user to obtain, estimate, embrace, and operate on data.

Python and R both languages are having their vibrant communities, simplified tools, and packages.

Python includes the following packages:

• Numpy: The data structures are highly efficient and extend Python into a high-level language as it contains various data manipulation operations in its data structure.

• Pandas: It suits work with labeled data, which helps organize data and manipulate data in tabular data manipulation tasks.

• Matplotlib: It creates detailed graphs with simple and few lines of python codes, for an interactive data visualization.

• Scikit-learn: It works in various machine learning tasks like classification, regression, and clustering.

• Tensorflow: It is highly prevalent in machine learning and deep learning models.

R includes the following packages:

• Dplyr: Both in memory and out of memory easily work with tabular data, helping to solve data manipulation.

• Ggplot2: It is a powerful graphic language created by Hadley Wickham, in R package it becomes a standard plotting package, focusing on visualizing the data.

• Data.table: It simplifies the data aggregation and, in a less computing time, with minimal coding to manipulate the dataset.

• Tidyverse: It is a collection of R packages. Tidyverse is not possible without R, as it uses it to collect the packages to clean, process, model, and data visualization. Shiny: It is an RStudio package that used to create or build highly interactive webpages.

• Caret: It is used for data manipulation and pre-processing with its collective tools and function; it also specializes in predictive models and machine learning.

Discussion 2

Big Data Visualizing Tools uses contemporary R, Python, JavaScript, SQL, and a web development environment to produce interactive visualizations using variously accessible and freely available data sources. It enables researchers and programmers to explore Big Data in new ways using an emerging, intuitive interface and a rich set of tools. Visualization tools allow for visualizing data without downloading it and embedding it into a webpage. They can take any source data and display it in a compact, easy to navigate, and can produce visual representations of raw data quickly and easily (Tahsin & Hasan, 2020). These types of tools can be done on any device or web browser. As people create more data-driven applications, they will more readily use data visualization tools to analyze the data and make sense of it quickly. Using data visualization tools is a great way to learn how to use visualizations. They are used as an educational tool for people of all skill levels. The tools can be used as learning tools or for advanced analytics and data management tasks (Tahsin & Hasan, 2020).

Python is a general-purpose programming language that is developed by Python Software Foundation. A programming language is a program written in one programming language that is intended to be used by a vast number of people on all kinds of different kinds of computer systems, because they all use different programming languages, such as Windows, Linux, and Apple OSX. So the problem in designing programming language is that one program must be interpreted or written in multiple programming languages, each one understood by a different group of people. R is widely used for learning data structures for programming and statistical analysis. Many statistical packages in Python are built on top of R. R is suitable for primary and intuitive programming, which does not require special skills and even more that may be perceived as non-intellectual (Tahsin & Hasan, 2020).

Python is suitable for particular problems where their complexity cannot be understood at first glance. In these environments, R is often running through a script shell. Numeric precision R and Python differ in this respect. In R, input data are typically numerical with numeric amounts. It makes it easier to work with variables without maintaining exact lengths. It is not compatible with several math packages. Numeric precision matters a lot for the accuracy of numeric calculations. R and Python both have excellent packages for building APIs and web scraping that efficiently works with big data sets (Yang, Aronson & Ahn, 2020).

Please make sure two response posts to given discussions substantive. A substantive post will do at least TWO of the following:

1.Ask an interesting, thoughtful question pertaining to the topic

2.Answer a question (in detail) posted by another student or the instructor

3.Provide extensive additional information on the topic

4.Explain, define, or analyze the topic in detail

5.Share an applicable personal experience

6.Provide an outside source (for example, an article from the UC Library) that applies to the topic, along with additional information about the topic or the source (please cite properly in APA)

7.Make an argument concerning the topic.

8.At least one scholarly source should be used in the initial discussion thread. Be sure to use

information from your readings and other sources from the UC Library. Use proper citations and references in your post.