Peerreview.docx

Peer review

1. Vikas Kanamarlapudi

Week 2 Discussion

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The data which is being heaped on a large scale in the current world is impossible to compute with the natural mathematical formulations. To be precise, statistical methods are preferable to compute and process the data. But that too is not possible with the manual or general efforts and they need some technical support. Therefore, statistical programs like Java, C, C++, Python, R, and "S" are some of the languages which will provide inbuilt applications and will be equipped with all the tools that help people to perform statistical operations on the data. Indeed, statistical programs will help companies and even countries to conduct surveys and scientists can perform research operations using statistical operations effectively.

R is one of the statistical programming languages that are capable of working efficiently and equally when compared to C, C++, Java, python, and "S" to provide various tools and tactics which can accurately compute and process the information.

Advantages of R language

R is one of the basic statistical programming languages in which it is open-source. In addition to this, the R language is independent of the platform or Operating System. So that every user is capable of tiring the services of R language. Similarly, the R language is used to enhance the value of data by clustering and classification where unorganized data will get converted into structured information. Thereby, the R language will perform data wrangling. Visualization of the data through time series plots and other charts will also be facilitated by R and these are advantages of R in comparison with other statistical programming languages (Lemenkova, 2019).

Disadvantages of R

R is not as capable of other statistical languages in terms of the origin of the data and security. On top of that, the R language is restricted to the size of the data or inputs provided to it. The larger the size of the data, the lesser will be the performance of the program. Moreover, the R program consumes time to process the information where the user has less scope to acquire the output within the expected time if the data is large. These are the disadvantages of R language when compared to other statistical programming languages (Cazzola & Shaqiri, 2017).

References

Cazzola, W., & Shaqiri, A. (2017). Open Programming Language Interpreters. The Art, Science, And Engineering of Programming, 1(2). https://doi.org/10.22152/programming-journal.org/2017/1/5

Lemenkova, P. (2019). STATISTICAL ANALYSIS OF THE MARIANA TRENCH GEOMORPHOLOGY USING R PROGRAMMING LANGUAGE. Geodesy and Cartography, 45(2), 57-84. https://doi.org/10.3846/gac.2019.3785

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2.Devi Priya Girija Raveendran Nair 

Discussion 2

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Statistical tools are very important for data analysis and visualization.  Especially when we are dealing with large quantity of data.  If we need to show the statistical diagram of population of a country by height or age group that will be a huge quantity of data which is not easy with manual work.  R is most used language by Data Scientist.

R is an open source programing language.  There is no license required to install R and everyone who wants to use R can install in their system.  R is more functional compare with Python. R has more data analysis and Python use classes to perform.  R have built in analysis and Python relies on package. 

There are some disadvantages of R, R is a slow programming language compare with Python.  R is good for incremental data, but Python is better for data manipulation.  R language have high dependency on library.  Python do not have as many libraries as R. 

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3.Kavya Kakumanu 

Week 2 Discussion

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The process that generates the power of an AI is similar to the process that generates the power of a natural process. The processes that generate the power of an AI can be defined as the processes that have the power to give a real-world result to the human actor. In the real world, the power of an AI is not given to the human actor because, in this case, the outcome could only be obtained by the actor and not by the process. Now that the processes that are not being evaluated have been evaluated by the player and given a real-world outcome, it becomes possible to generate an autonomous AI (Laursen & Thorlund, 2016). In this article, examine how can create such a process.

Machine learning is a software that uses statistical models to learn from data. Like humans, robots and computers learn and process data in various ways. A statistical model is a collection of rules and equations that allows them to generate the data in a specified way automatically. There are so many different statistical models, but deep learning is one of the more common. Deep learning is a method of training a computer to process data using mathematical models that are trained on a set of data that is presented to it. For example, the machine learning method uses a set of data to learn a function to extract parameters out of that set and calculate another function to extract its output. The deep learning approach tries to take the values of variables out of the data to be used in a function to solve for those values, and for how long (Laursen & Thorlund, 2016).

Machine learning is a method of training a computer to process data using mathematical models that are trained on a set of data that is presented to it. For example, the machine learning method uses a set of data to learn a function in order to extract parameters out of that set and calculate another function to extract its output. The deep learning approach tries to take the values of variables out of the data to be used in a function to solve for those values, and for how long (Trieu, 2017). Machine learning is a machine that has an input stream.

References

Laursen, G. H., & Thorlund, J. (2016). Business analytics for managers: Taking business intelligence beyond reporting. John Wiley & Sons.

Trieu, V. H. (2017). Getting value from Business Intelligence systems: A review and research agenda. Decision Support Systems, 93, 111-124.

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4.Tejaswini Dondapati 

tejaswini week 2

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AI

Discuss the process that generates the power of AI and discuss the differences between machine learning and deep learning.

 The generation of Artificial intelligence occurs through a simulation of human intelligence processes using computer systems and other machines.   Using algorithms, it becomes possible to create rules and data acquisition process such that they determine how they will turn data into actionable information.

Besides, there us integration of voluminous quantities of information with fast, iterative processing and intelligent algorithms that allow the software to learn in an automatic way from data features and data patterns.  It involves a lot of theories, technologies, and approaches and principal subfields like deep learning, cognitive computing, computer vision, a neural network, and machine learning (Sharda, Delen, & Turban, 2019). Other technologies involved are the internet of things, graphical processing units, advanced algorithms, and application programming interfaces.

Machine learning involves an approach that utilizes existent data to make it possible for the algorithm to enhance operations and evaluation of data. It automates analytical model building through the use of approaches such as operations research, statistics, neural networks, and physical to establish concealed insights in information without openly being programmed for the place to search for it or the conclusion to make (Sharda, Delen, & Turban, 2019).

On the other hand, deep learning is the utilization of voluminous neural networks with a lot of layers of unit processes, taking advantage of the progress in computing abilities and better training approaches to learn complicated patterns of large quantities of information. Deep learning has applications such as speech recognition and image recognition.

 

 

References

Sharda, R., Delen, D., & Turban, E. (2019). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support. 11th Ed. Harlow: Pearson Education Limited.

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