scholarly journals An Intelligent Data-Driven Analytics System for Operation Management, Budgeting, and Resource Allocation using Machine Learning and Data Analytics

2021 ◽  
Author(s):  
Dele Fei ◽  
Yu Sun
Keyword(s):  
Data Analytics ◽  
Data Science ◽  
Forecast Model ◽  
Data Driven ◽  
R Programming Language ◽  
R Shiny ◽  
Customer Type ◽  
Web App ◽  
R Programming ◽  
Product Warranty

This is a data science project for a manufacturing company in China [1]. The task was to forecast the likelihood that each product would need repair or service by a technician in order to forecast how often the products would need to be serviced after they were installed. That forecast could then be used to estimate the correct price for selling a product warranty [2]. The underlying forecast model in the R Programming language for all of the companies products is established. In addition, an interactive web app using R Shiny is developed so the business could see the forecast and recommended warranty price for each of their products and customer types [3]. The user can select a product and customer type and input the number of products and the web app displays charts and tables that show the probability of the product needing service over time, the forecasted costs of service, along with potential income and the recommended warranty price.

Big Data Analytics in Healthcare Sector

2019 ◽  
pp. 94-106 ◽  
Author(s):  
Sheik Abdullah A. ◽  
Selvakumar S. ◽  
Parkavi R. ◽  
Suganya R. ◽  
Abirami A. M.
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Implementation Process ◽  
Healthcare Sector ◽  
Data Preparation ◽  
Healthcare Data ◽  
R Programming ◽  
Python Programming

The importance of big data over analytics made the process of solving various real-world problems simpler. The big data and data science tool box provided a realm of data preparation, data analysis, implementation process, and solutions. Data connections over any data source, data preparation for analysis has been made simple with the availability of tremendous tools in data analytics package. Some of the analytical tools include R programming, python programming, rapid analytics, and weka. The patterns and the granularity over the observed data can be fetched with the visualizations and data observations. This chapter provides an insight regarding the types of analytics in a big data perspective with the realm in applicability towards healthcare data. Also, the processing paradigms and techniques can be clearly observed through the chapter contents.

scholarly journals Data science and analytics in agricultural development

2021 ◽  
Vol 22 (SE) ◽  
pp. 9-19
Author(s):  
Karamveer Singh Sidhu ◽  
Ramandeep Singh ◽  
Snehdeep Singh ◽  
Gunjot Singh
Keyword(s):  
Data Analytics ◽  
Agricultural Development ◽  
Data Science ◽  
Data Driven ◽  
High Population ◽  
Food And Agriculture ◽  
Current Population ◽  
Food And Agriculture Organization ◽  
Agricultural Produce ◽  
The World

The consistent advancement of innovation has implied information and data being created at a rate, not at all like ever previously, and it's just on the ascent. The world makes an extra 2.5 quintillion bytes of information every year. The demand for individuals talented in investigating, deciphering, and utilising this information is now high and is set to become exponential over the coming years. The total populace is relied upon to arrive at 9.7 billion by 2050 from the current population of 7.8 billion. The Food and Agriculture Organization (FAO) has predicted that the development of farming must be expanded by 70% to provide for the extended interest. Data-driven agriculture choices can be a potent technology to manage the needs of this much high population, as this technology gives higher efficiency, rehearses support-ability, and even assists with giving straightforwardness to purchasers and consumers needing to find out about their food as reported in the studies. The current and future interests will require more data researchers, data engineers, data specialists, and chief data Officers.  This paper tries to examine the need, use, role, and issues faced by data science and data analytics to improve the quality as well as quantity of Agricultural produce thereby leading to an increase in production, a decrease in costs, and overall sustainability.

scholarly journals Contemporary Data Science for Finance Students: A Comparative Study of Essential Features of Commonly Used Statistical Software

2020 ◽  
Vol 6 (2) ◽  
pp. 19-24
Author(s):  
Madalina Viorica Manu ◽  
Ilie Vasile
Keyword(s):  
Comparative Study ◽  
Data Science ◽  
Relevant Literature ◽  
Global Scale ◽  
Statistical Software ◽  
R Programming Language ◽  
Business Excellence ◽  
R Programming ◽  
Romanian Literature ◽  
Basic Features

In this paper, we compare some of the essential traits of the software preferred by researchers, students, and professors, such as R or RStudio, or Matlab. In order to fill the gap in the Romanian literature and help finance students in choosing proper tools according to the research purpose, this comparative study aims at bringing a fresh, useful perspective in the relevant literature. In Romania, the use of R was the focus of several international conferences on official statistics held in Bucharest, and others having business excellence, innovation, and sustainability as purpose, while Eviews is recommended and taught by the Romanian professors. At this time, at a global scale, R programming language is considered the lingua franca of data science, as common statistical software used both in corporations and academia. In this paper, I analyze the basic features of such software, with the purpose of application in finance.

scholarly journals Prediction of Heart Disease and Diabetes Using Naive Bayes Algorithm

2021 ◽  
pp. 447-453
Author(s):  
Ninad Marathe ◽  
Sushopti Gawade ◽  
Adarsh Kanekar
Keyword(s):  
Heart Disease ◽  
Potential Problem ◽  
University Of California ◽  
Test Report ◽  
Ui Design ◽  
R Programming Language ◽  
R Shiny ◽  
R Programming ◽  
Bayes Algorithm ◽  
The University

Based on the test report values, diagnose a potential problem. The patient's report can be entered as feedback by the doctors (Sugar level, Age, Blood pressure, etc.). Through evaluating the available data collection, we can predict whether the patient has heart disease or diabetes using the method. Apart from that, we use Rstudio's R shiny addon for Web UI design. As a coding language, we use the R programming language. The Rstudio IDE was used. The datasets were obtained from the University of California at Irvine's repository.

scholarly journals ChR: Dynamic Functional Constraints Checking in R

2017 ◽  
Vol 9 (1) ◽  
pp. 65-78
Author(s):  
Konrad Grzanek
Keyword(s):  
Large Scale ◽  
Data Science ◽  
R Programming Language ◽  
Large Scale Data ◽  
Dynamic Type ◽  
Gradual Typing ◽  
R Programming ◽  
Learning Projects ◽  
Time Type ◽  
Scale Data

Abstract Dynamic typing of R programming language may issue some quality problems in large scale data-science and machine-learning projects for which the language is used. Following our efforts on providing gradual typing library for Clojure we come with a package chR - a library that offers functionality of run-time type-related checks in R. The solution is not only a dynamic type checker, it also helps to systematize thinking about types in the language, at the same time offering high expressivenes and full adherence to functional programming style.

scholarly journals Data Science and Predictive Analytics: Biomedical and Health Applications Using R

2020 ◽  
Vol 108 (2) ◽  
pp. 334
Author(s):  
Benjamin H. Saracco
Keyword(s):  
Programming Language ◽  
Data Science ◽  
Predictive Analytics ◽  
Health Sciences ◽  
Online Course ◽  
Data Sets ◽  
R Programming Language ◽  
R Programming ◽  
Health Applications ◽  
Analyze Data

Ivo D. Dinov’s Data Science and Predictive Analytics: Biomedical and Health Applications Using R is a comprehensive twenty-three-chapter text and online course for burgeoning or seasoned biomedical and/or health sciences professionals who analyze data sets using the R programming language.

scholarly journals Membuat Berbagai Grafik dengan STATCAL-VISUALIZATION

2018 ◽  
Author(s):  
Prana Ugiana Gio ◽  
Rezzy Eko Caraka ◽  
Dian utami sutiksno ◽  
Ansari Saleh Ahmar
Keyword(s):  
Programming Language ◽  
Application Program ◽  
R Programming Language ◽  
R Shiny ◽  
Program Application ◽  
R Programming

STATCAL-VISUALIZATION is a application program designed to make various graph easily. STATCAL-VISUALIZATION is designed using r programming language, in RStudio, with r shiny package as the main package. STACAL-VISUALIZATION program application can be downloaded in www.statcal.com.

Descriptive Data Analytics on Dinesafe Data for Food Assessment and Evaluation Using R Programming Language

2021 ◽  
pp. 485-507
Author(s):  
Ajinkya Kunjir ◽  
Jugal Shah ◽  
Vikas Trikha
Keyword(s):  
Data Analytics ◽  
Inspection System ◽  
Descriptive Data ◽  
Assessment And Evaluation ◽  
R Programming Language ◽  
Digital Era ◽  
The People ◽  
Open Source Data ◽  
R Programming ◽  
The City

In the digital era of the 21st century, data analytics (DA) can be highlighted as 'finding conclusions based on observations' or unique knowledge discovery from data (KDD) in form of patterns and visualizations for ease of understanding. The city of Toronto consists of thousands of food chains, restaurants, bars based all over the streets of the city. Dinesafe is an agency-based inspection system monitored by the provincial and municipal regulations and ran by the Ministry of Health, Ontario. This chapter proposes an efficient descriptive data analytics on the Dinesafe data provided by the Health Ministry of Toronto, Ontario using an open-source data programming framework like R. The data is publicly available for all the researchers and motivates the practitioners for conveying the results to the ministry for betterment of the people of Toronto. The chapter will also shed light on the methodology, visualization, types and share the results from the work executed on R.

STATISTICAL CALCULATOR

2018 ◽  
Author(s):  
Prana Ugiana Gio ◽  
Rezzy Eko Caraka
Keyword(s):  
R Package ◽  
Process Data ◽  
Web Based ◽  
Application Program ◽  
R Programming Language ◽  
Statistical Application ◽  
R Shiny ◽  
R Packages ◽  
R Programming ◽  
Mozilla Firefox

STATCAL is an free statistical application program that is designed using R programming language, in RStudio. STATCAL uses various R packages to perform graphical and statistical analysis. STATCAL is a web-based statistical application program. It means that STATCAL uses a browser, such as Google Chrome, Mozilla Firefox, etc, as a place or media to process data. R shiny package is a main package in STATCAL. R shiny package is an R package that can be used to create an interactive web-based application. STATCAL is created by Prana Ugiana Gio and Rezzy Eko Caraka on 2017.

scholarly journals An Optimised Method for Fetching and Transforming Survey Data based on SQL and R Programming Language

2019 ◽  
Vol 16 (2(SI)) ◽  
pp. 0436
Author(s):  
Hasan Et al.
Keyword(s):  
Programming Language ◽  
Data Science ◽  
Query Language ◽  
Major Drawback ◽  
Relational Models ◽  
R Programming Language ◽  
Survey System ◽  
R Programming ◽  
Improved Accuracy

The development of information systems in recent years has contributed to various methods of gathering information to evaluate IS performance. The most common approach used to collect information is called the survey system. This method, however, suffers one major drawback. The decision makers consume considerable time to transform data from survey sheets to analytical programs. As such, this paper proposes a method called ‘survey algorithm based on R programming language’ or SABR, for data transformation from the survey sheets inside R environments by treating the arrangement of data as a relational format. R and Relational data format provide excellent opportunity to manage and analyse the accumulated data. Moreover, a survey system based on structured query language and R programming language is designed to optimize methods to manage survey systems by applying large features offered via combining multi data science languages. The experiments verified enhancements of flexibility, technical tools, and data visualization features employed to process the collected data from different aspects; therefore, the proposed approach demonstrates a simple case study to enhance the evaluation requirements of the proposed technique. Finally, the estimated results of this research can be used to improve the methods of information management on different aspects such as survey systems and other data models that hold the relational and non-relational models using SABR. This method demonstrated improved accuracy of data collected, reduced data processing time and arranged data to the willing model.

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