attaached.
Discussion-1 150 words with one reference
Utilization of R Vs. Python
R is mainly seen to be utilized for doing the analysis in a statistical manner whereas Python is mainly utilized for providing a maximum approach to data science in a general manner. R could be implemented in the system for developing the software that is based on statistical and analysis of the data (Kaya, Agca, Adiguzel & Cetin, 2019). Python has the ability to help users by managing all kinds of the various formats of the data. The benefits of using R are it is seen to be highly compatible and when it comes to plotting in the graph, it always provides a quality. The drawback that could be seen with this programming language is handling the data and providing primary security is seen to be lacking. In the case of Python, the productivity is seen to be improved and it is easy to read, learn, and write. These are some of the important advantages of Python (Jhamb, Gupta, Shukla, Mearaj & Agarwal, 2020). This language over provides some drawbacks such as accessing the database is lacking, speed is also found to be slow, and did not work efficiently.
Providing examples
Example of R programming
Check leap year
Input
# Program to check if the input year is a leap year or not
year = as.integer(readline(prompt=”Enter a year: “))
if((year %% 4) == 0) {
if((year %% 100) == 0) {
if((year %% 400) == 0) {
print(paste(year,”is a leap year”))
}
else {
print(paste(year,”is not a leap year”))
}
} else {
print(paste(year,”is a leap year”))
}
} else {
print(paste(year,”is not a leap year”))
}
Output Enter a year: 1900
[1] “1900 is not a leap year”
Example of Python
Convert Celsius into Fahrenheit
Input
# Python Program to convert temperature in celsius to Fahrenheit
# change this value for a different result
celsius = 37.5
# calculate fahrenheit
fahrenheit = (celsius * 1.8) + 32
print (‘%0.1f degree Celsius is equal to %0.1f degree Fahrenheit’ %(celsius,fahrenheit))
Output
37.5 degree Celsius is equal to 99.5 degrees Fahrenheit
My point of view
As per my viewpoint, R programming is always would be a better option and understand better when it comes to visualizing the data with the big data analysis procedure as the objective of R is found to be statistics and data analysis procedure and this always gives them a better chance in providing that support in the system. It would be valuable in visualizing the data as the graphs that have been used by R programming always found to be beautiful and effective and this makes the difference with the other programming language. I have not used any of these languages but I could predict its utilization for analyzing the big data in the system. This programming language has the ability to make things happen in a different manner.
References
Jhamb, S., Gupta, R., Shukla, V. K., Mearaj, I., & Agarwal, P. (2020, January). Understanding Complexity in Language Learning Through Data Visualization Using Python. In 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM) (pp. 268-274). IEEE.
Kaya, E., Agca, M., Adiguzel, F., & Cetin, M. (2019). Spatial data analysis with R programming for environment. Human and ecological risk assessment: An International Journal, 25(6), 1521-1530.
Discussion-2 150 words with one reference
Data visualization entails the pictorial representation of data by use of visual element to enhance information understanding. Big data visualization involve implementing contemporary tools to illustrate different patterns in the data. The techniques used in big data visualization are more improvised; thus, the illustrative display graphics that are beyond the pie charts and bar charts (Hassan & Elragal, 2017). The tools used in big data visualization provide a broad visualization style, can be used to visualize large datasets and are easy to use. Some of the visualization techniques used for big data visualization include R and python tools.
R is a tool for visualization of big data and is a free software environment used for statistical computing backed up with R foundation. The method is mostly used by statisticians in the processes of data mining and analyzing the data.R programming language involves a broad community, and thus new libraries in R, are added continuously at their catalogues (Huddar & Kulkarni,2018).R language is advantageous because it leads to data manipulation and statistical modelling. However, the main drawback for R programming is that it has complicated language and has a lesser speed. Example of R programming is the R program used to determine a leap year.
Python programming language is another tool used in big data visualization because it has a general-purpose in analyzing. For example, Python can be used to develop software prototypes and various applications in data science. Python differ with R programing language in that; it has a general-purpose, that is, it allows one to focus on the central functionality through being careful to the other programming tasks (Kaleb et al.,2019). The benefits of Python is that, it is versatile that is, it focuses on readability and is easy to learn. The disadvantage associated with Python includes, it has speed limitation and involves problems when threading. When performing data analysis in work, R programming language can be used where there is a need to manipulate data. However, the python method can be used for large datasets due to its broad general-purpose such as web and software creation.
References
Hassan, A., & Elragal, A. (2017). Big Data Visualization Tool: a Best-Practice Selection Model. In 10th IADIS International Conference on Information Systems 2017, Budapest, 10-12 April 2017 (pp. 59-68). Institute of Electrical and Electronics Engineers (IEEE).
Huddar, M. R., & Kulkarni, R. V. (2018). Role of R and Python in Data Science. RESEARCH JOURNEY, 32.
Kaleb, K., Vesztrocy, A. W., Altenhoff, A., & Dessimoz, C. (2019). Expanding the Orthologous Matrix (OMA) programmatic interfaces: REST API and the packages for R and Python OmaDB.
Discussion-3 100 words
Physical security describes all the measures aside to prevent unauthorized access of facilities in an organization. Physical security ensures key areas such as data centers to have security lightning to provide illumination and ensure easy identification of objects and individuals in the organization premises. Security lightning aims at causing a psychological deterrent unauthorized persons that may be intending to avail themselves or present their activities illegally at data centers (Wrange & Bengtsson, 2019). Internal lighting data centers focuses to achieve physical aesthetic. The importance of internal lighting in data centers is vital because it helps to prevent accidents that may cause loss of information. Besides that, internal lighting at data centers eases identification of individuals present at data centers.
Data centers usually contain important information that is reliable to individuals in and out of the company. Information at data centers is trusted by many individuals thus guarding data centers through external lighting to protect sabotage or loss of information External lighting is important in data centers because it makes individuals develop a feeling of safety to their information (De Jong, 2016). Also, external lighting is important because it eases the investigation of issues that might have happened outside the data centers. Moreover, having external lighting installed in the organization premises is also important because it allows security to individuals while outside and gets their intention of the persons through reaction and behavior.
The article focusing on security lighting is “Smart Lights Enhance Home Security and Shine a Light on Crime” and its synopsis is as follows. The author discusses the impacts of lighting and how it prevents crime. According to the author, the technique is effective and normal to ensure security in the organization (Cericola, 2019). However, the method is considered as invaluable because it affects the other form of conventional security such as the use of locks and alarms. In the article, the author emphasizes on the need to improve security lighting.
References
Cericola, R. (2019, September 23). Smart Lights Enhance Home Security and Shine a Light on Crime. Retrieved from
https://www.nytimes.com/2019/09/19/smarter-living/wirecutter/smart-lights-enhance-home-security-and-shine-a-light-on-crime.html
De Jong, M. (2016). Spanish Security Forces, Anti-terrorism and the Internal and External Security of Spain, 1959–1992. In NL ARMS Netherlands Annual Review of Military Studies 2016 (pp. 325-349). TMC Asser Press, The Hague.
Wrange, J., & Bengtsson, R. (2019). Internal and external perceptions of small state security: the case of Estonia. European Security, 28(4), 449-472.
Discussion-4 100 words
Importance of Internal and External Security Lighting at Datacentre Facilities
Data centres are usually facilities where information is stored in the servers and the information belongs to quite a large number of people. The datacentres contains crucial data that many people put it there trusting it will be secured from intruders who might use the information to hurt the economy as well as individuals. Due to this reason, data centres are usually heavy guarded where there are strategic external and internal lightings. This well advanced security lighting both inside and outside the data centres provides a level of illumination that is used to enable the security officers identify unauthorized intruders as well to avoid accidents (Mctxsheriff.org. 2017).
The internal lighting is important in providing light to the people working in the data centres and avoids accidents that may lead to bodily harm as well as destruction or sabotaging information or part of information stored. The internal light enables the identification of workers since in case of a break in, every person is supposed to recognize their colleagues thus easily identifying intruders (Ericola, 2019). The external lighting on the other side enhances the safety of the centres as it enables the security to analyse and investigate activities taking place outside the data centres. It also enables communication with someone outside the centres for easier identification and determination whether they are to be trusted and allowed to get in.
In the synopsis of an article that I used, I found how the author describes how the lighting enhances security and why it is an important security feature in the datacentres. The author describes how the lighting strategies are effective and a normal technology for protecting the information inside the datacentres (Mctxsheriff.org. 2017). This strategy generates psychological deterrent to the intruders who might try to entre illegally to the datacentres for illegal activities.
References
Ericola, R. (2019, September 23). Smart Lights Enhance Home Security and Shine a Light on Crime. Retrieved from
https://www.nytimes.com/2019/09/19/smarter- living/wirecutter/smart-lights-enhance-home-security-and-shine-a-light-on-crime.html
Mctxsheriff.org. (2017). Security Lighting. Retrieved from
http://www.mctxsheriff.org/residents/lighting.php
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