A substantive post will do at least TWO of the following: Ask an interesting, thoughtful question pertaining to the topic Answer a question (in detail) posted by another student or the instructor P

Discussion 1:

   Both Rand Python are considered to be open-cause programming languages with a big society.  R is normally used when it comes to statistical evaluations as pythons are used to give additional overall concept to information science.  The two designs a terminology of programming language based on information science. Studying R and Python is considered to be ideal resolution since they require a time investment and luxury of the like are not present for everybody.  Python is overall-aimed language with a readable syntax. On the other hand, R is developed through statisticians and covers their certain languages (Sarmento & Costa, 2017). 

R

The benefits of R is that it is considered as one of the richest eco-systems to undertake information evaluations.  It is very easy to look for a library of whatever evaluations you wants to undertake.   Having variety of analysis makes R to the prioritized selection for statistical evaluation mostly for certain analytical operations.  It’s very easy to communicate as well as present its outcome.

Its disadvantage is that it can only be differentiate from other statistical item through its final product.

Python

Python is considered to undertake the same role as R such as information wrangling, engineering, choosing features. It is used to deploy and establish machine learning when it comes to large scales. Its codes are easier to retain compared to R.  Python makes its reliability and accessibility much easier compared to R. Python is the best when in need of analyzing your result for an application or website (Xia, McClelland & Wang, 2010).

In conclusion, both R and Python are programming languages. R helps in analyzing information as outcome while Python lacks library for analysis options.  This differentiates the two GUI tools used in Big Data Visualization tools.

References

Sarmento, R., & Costa, V. (Eds.). (2017). Comparative approaches to using R and python for statistical data analysis. IGI Global.

Xia, X. Q., McClelland, M., & Wang, Y. (2010). PypeR, A Python package for using R in Python. Journal of Statistical Software, 35(c02).

Discussion 2:

Today’s generation industries were all investing in Big data. The importance of Big data is improved since companies generating massive data and storing remains challenge. However, gathered data is used further to interpret, using these insights via interpretation tools representing graphs, charts etc., making decisions are possible. Visualizing data is not that easy, it all does is representing complex things into understandable view. Since huge amounts of data is generated, process data is easy but when considered in high volumes things get difficult. Benefits occurred with data visualization is Time savings, reduced burden, increased returns on investments, provide self-service capabilities to end users, improved collaborations and most important improved decision making. Big Data can be structures, semi-structured and unstructured and visualizations tools help in processing creates parallelization (Ali et al., 2016).

            The main difference is R mainly used statistical representation of data whereas python has readable syntax used by every individual programmer. R allows any sort of analysis required for data analysis using its 12000 packages. Thus, it is considered to be one of best framework to perform data analysis (Gallagher & Trendafilov, 2018). python is mainly used to process data at large scale. Codes written in python for executing a task is more compatible and easier than R. but when it comes to functionalities, R and python offers data wrangling, cleansing, scrapping etc. that are performed on available data.

            For example, As programmer developer duties I was asked to extract data from slack messenger from certain date and time. Here I used python to create the task, to my experience python is easily readable and lot of helping sites that makes your task easy to implement with advanced packages. As mentioned, R is used in statistical representation, data analytics and research. I have seen it gaining more popularity with Big Data.

            If you were asked to choose between R and python which one u choose and why?

References:

Ali, S. M., Gupta, N., Nayak, G. K., & Lenka, R. K. (2016). Big data visualization: Tools and challenges. 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), Contemporary Computing and Informatics (IC3I), 2016 2nd International Conference On, 656–660. https://doi.org/10.1109/IC3I.2016.7918044

Gallagher, M., & Trendafilov, R. (2018). R Vs. Python: Ease of Use and Numerical Accuracy. Journal of Business & Accounting, 11(1), 117–126.