Review of data analytic teaching cases, have we covered enough?

2021 ◽  
Author(s):  
Robyn L Raschke ◽  
Kimberly F. Charron
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Accounting Education ◽  
Data Preparation ◽  
Business Partner ◽  
Education Literature ◽  
Analytic Process ◽  
Principal Tool ◽  
Teaching Cases ◽  
Data Analytic

Big Data and data analytics are changing the accounting profession requiring different skills. To prepare students, faculty are tasked with teaching data analytics and the tools used to perform data analysis. This study examines data analytic teaching cases across the curriculum from peer-reviewed accounting education literature using the lens of the data analytic process. We include the breadth of tools students employ to complete the cases that will give students the experience needed to be a valued business partner. We find that the majority of teaching cases focus on analysis and communication, while few cover necessary steps prior to analysis such as extraction and data preparation. We also find that Excel is the principal tool used, while other much needed tools are rarely employed in teaching cases. Our findings provide an opportunity for academics to create new teaching cases to provide students with comprehensive experiences throughout the data analytics cycle.

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.

Discovering Knowledge Hidden in Big Data from Machine-Learning Techniques

2017 ◽  
pp. 167-183 ◽  
Author(s):  
Adiraju Prashantha Rao
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Decision Makers ◽  
Machine Learning Techniques ◽  
Human Beings ◽  
Raw Data ◽  
Learning Techniques ◽  
Data Analytic ◽  
Information Growth

As the speed of information growth exceeds in this new century, excessive data is making great troubles to human beings. However, there are so much potential and highly useful values hidden in the huge volume of data. Big Data has drawn huge attention from researchers in information sciences, policy and decision makers in governments and enterprises. Data analytic is the science of examining raw data with the purpose of drawing conclusions about that information. Data analytics is about discovering knowledge from large volumes data and applying it to the business. Machine learning is ideal for exploiting the opportunities hidden in big data. This chapter able to discover and display the patterns buried in the data using machine learning.

Relevance of big data to forensic accounting practice and education

2019 ◽  
Vol 34 (3) ◽  
pp. 268-288 ◽  
Author(s):  
Zabihollah Rezaee ◽  
Jim Wang
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Accounting Education ◽  
Content Type ◽  
Accounting Practice ◽  
Accounting Curriculum ◽  
Forensic Accounting ◽  
Accounting Courses ◽  
Challenges And Opportunities ◽  
Practical Implications

Purpose This paper aims to examine the relevance of Big Data to forensic accounting practice and education by gathering opinions from a sample of academics and practitioners in China. Design/methodology/approach The authors conduct a survey of academics and practitioners regarding the desired demand, importance and content of Big Data educational skills and topics for forensic accounting education to effectively respond to challenges and opportunities in the age of Big Data. Findings Results indicate that the demand for and interest in Big Data/data analytics and forensic accounting will continue to increase; Big Data/data analytics and forensic accounting should be integrated into the business curriculum; many of the suggested Big Data topics should be integrated into forensic accounting education; and some attributes and techniques of Big Data are important in improving forensic accounting education and practice. Research limitations/implications Readers should interpret the results with caution because of the sample size (95 academics and 103 practitioners) and responses obtained from academics and practitioners in one country (China) that may not be representative of the global population. Practical implications The results are useful in integrating Big Data topics into the forensic accounting curriculum and in redesigning the forensic accounting courses/programs. Social implications The results have implications for forensic accountants in effectively fulfilling their responsibilities to their profession and society by combating fraud. Originality/value This study provides educational, research and practical implications as Big Data and forensic accounting are advancing.

Exploring Big Data Analytic Approaches to Cancer Blog Text Analysis

2019 ◽  
Vol 14 (4) ◽  
pp. 1-20 ◽  
Author(s):  
Viju Raghupathi ◽  
Yilu Zhou ◽  
Wullianallur Raghupathi
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Word Association ◽  
Big Data Analytics ◽  
Text Analytics ◽  
Hadoop Mapreduce ◽  
Cancer Management ◽  
Exploratory Approach ◽  
Clustering And Classification ◽  
Data Analytic

In this article, the authors explore the potential of a big data analytics approach to unstructured text analytics of cancer blogs. The application is developed using Cloudera platform's Hadoop MapReduce framework. It uses several text analytics algorithms, including word count, word association, clustering, and classification, to identify and analyze the patterns and keywords in cancer blog postings. This article establishes an exploratory approach to involving big data analytics methods in developing text analytics applications for the analysis of cancer blogs. Additional insights are extracted through various means, including the development of categories or keywords contained in the blogs, the development of a taxonomy, and the examination of relationships among the categories. The application has the potential for generalizability and implementation with health content in other blogs and social media. It can provide insight and decision support for cancer management and facilitate efficient and relevant searches for information related to cancer.

scholarly journals Approximate computation for big data analytics

2021 ◽  
pp. 1-8
Author(s):  
Shuai Ma ◽  
Jinpeng Huai
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Optimal Solution ◽  
Approximate Computation ◽  
Approximate Query Processing ◽  
Data Approximation ◽  
Approximation Techniques ◽  
Approximate Query ◽  
Data Analytic

Over the past a few years, research and development has made significant progresses on big data analytics. A fundamental issue for big data analytics is the efficiency. If the optimal solution is unable to attain or unnecessary or has a price to high to pay, it is reasonable to sacrifice optimality with a "good" feasible solution that can be computed efficiently. Existing approximation techniques can be in general classified into approximation algorithms, approximate query processing for aggregate SQL queries and approximation computing for multiple layers of the system stack. In this article, we systematically introduce approximate computation, i.e. , query approximation and data approximation, for efficient and effective big data analytics. We explain the ideas and rationales behind query and data approximation, and show efficiency can be obtained with high effectiveness, and even without sacrificing for effectiveness, for certain data analytic tasks.

scholarly journals Underpinnings of Big Data Analytics and Its Applications

2020 ◽  
pp. 75-81
Author(s):  
Anand Kumar Pandey ◽  
Rashmi Pandey ◽  
Ashish Tripathi
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Research Issues ◽  
New Developments ◽  
New Ideas ◽  
Analytical Assessment ◽  
Data Analytic ◽  
Big Data Technology

Big data and Data Mining are co-related to each other and also emphasize the phenomena of extracting and analysis useful data from considerable database. The concept of Big Data analytics plays a very significant role in several fields, such as Data Mining, Education and Training, cloud computing, E-commerce, healthcare and life science, Banking and Agriculture. Big data Analytic is a technique for looking at big set of data to expose hidden patterns. A large amount of data is continuously generated every day using modern information system and technologies. As a result this paper provides a platform to investigate applications of big data at various stages. In future, it come forward to be a required for an analytical assessment of new developments in the big data technology. In addition, it also explores a new and suitable outlook for researchers to expand the solution, based on the literature survey, challenges, new ideas and open research issues.

scholarly journals Studi On Big Data Analytics Framework in Smart City Context

2021 ◽  
Vol 328 ◽  
pp. 04022
Author(s):  
Rahmawati Dinda ◽  
Arief Assaf ◽  
Do Abdullah Saiful Saiful
Keyword(s):  
Big Data ◽  
Smart City ◽  
Data Analytics ◽  
Smart Cities ◽  
Big Data Analytics ◽  
Future Research ◽  
Technology Governance ◽  
Advantages And Disadvantages ◽  
The Government ◽  
Data Analytic

The issue of global urbanization, which is a separate problem faced by the government, is the very rapid growth of population density in cities. To face this challenge, the government launched a smart city project by targeting sustainable economic growth and improving the quality of life. Information and Communication Technology governance is the key to realizing a smart city. However, each of these I.C.T. tools produce large amounts of data known as Big Data. Data processing with the Big Data approach is becoming a trend in information systems to provide better public services and provide references in the policy-making process. However, to obtain important information in the scope of big data, a Big Data Analytics process is needed, also known as Big Data Value Chain. Extracting knowledge from the related literature can identify the characteristics of the big data analytic framework for smart cities. This paper reviews several big data analytic frameworks applied to smart cities. This paper is to find the advantages and disadvantages of each framework so that it can be a direction for future research

scholarly journals Exploring Big Data Analytic Approaches to Cancer Blog Text Analysis (Preprint)

2018 ◽  
Author(s):  
Viju Raghupathi ◽  
Yilu Zhou ◽  
Wullianallur Raghupathi
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Analytics ◽  
Word Association ◽  
Early Stage ◽  
Big Data Analytics ◽  
Free Text ◽  
Health Related ◽  
Data Analytic ◽  
Insight Into

BACKGROUND In recent years researchers have begun to realize the value of social media as a source for data that helps us understand health-related phenomena. Health blogs in particular are rich with information for decision-making. While there are web crawlers and blog analysis software that generate statistics related to blogs, these are relatively primitive and are not useful computationally to aid with the analysis and understanding of the social networks and medical blogs that are evolving around healthcare. There is a need for sophisticated tools to fill this gap. Furthermore, to our knowledge there are not many big data studies or applications in the text analytics of cancer blogs. This study attempts to fill this specific gap while analyzing cancer blogs. OBJECTIVE In this exploratory research, we examine the potential of applying big data analytic techniques to the analysis of blogs that exist in the cancer domain. Our objective is twofold: to extract from the blogs, patterns and insight about cancer diagnosis, treatment, and management; and to apply advanced computation techniques in processing large amounts of unstructured health data. METHODS We applied the big data analytics architecture of Hadoop MapReduce via the Cloudera platform to the analysis of cancer blog content, in order to extract patterns and insight on cancer diagnoses. We apply a series of algorithms to gain insight into the content and develop a vocabulary and taxonomy of keywords based on existing medical nomenclature. By applying a number of algorithms, we gained insight into the blog content. The study identifies, for instance, the most discussed topics as well as associations that relate to key phenomena RESULTS Using several text analytic algorithms, including word count, word association, clustering, and classification, we were able to identify and analyze the patterns and keywords in cancer blog postings. This gave insight into some of the key issues that are discussed in blogs such as the type of cancer (breast cancer being the dominant topic), diagnosis, treatments, and others. CONCLUSIONS In general, big data analytics has the potential to transform the way practitioners and researchers gain insight from health social media, especially those in free text, unstructured form. Big data analytics and applications in health-related social media are still at an early stage, and rapid acceleration is possible with the advancements in models, tools, and technologies.

scholarly journals “Big results require big ambitions”: big data, data analytics and accounting in masters courses

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Karen Mcbride ◽  
Christina Philippou
Keyword(s):  
Big Data ◽  
Critical Thinking ◽  
Data Analysis ◽  
Data Analytics ◽  
Thinking Skills ◽  
Accounting Education ◽  
Professional Qualification ◽  
Content Type ◽  
The Impact

Purpose Accounting education is re-inventing itself as technology impacts the practical aspects of accounting in the real world and education tries to keep up. Big Data and data analytics have begun to influence elements of accounting including audit, accounting preparation, forensic accounting and general accountancy consulting. The purpose of this paper is to qualitatively analyse the current skills provision in accounting Masters courses linked to data analytics compared to academic and professional expectations of the same. Design/methodology/approach The academic expectations and requirements of the profession, related to the impact of Big Data and data analytics on accounting education were reviewed and compared to the current provisions of this accounting education in the form of Masters programmes. The research uses an exploratory, qualitative approach with thematic analysis. Findings Four themes were identified of the skills required for the effective use of Big Data and data analytics. These were: questioning and scepticism; critical thinking skills; understanding and ability to analyse and communicating results. Questioning and scepticism, as well as understanding and ability to analyse, were frequently cited explicitly as elements for assessment in various forms of accounting education in the Masters courses. However, critical thinking and communication skills were less explicitly cited in these accounting education programmes. Research limitations/implications The research reviewed and compared current academic literature and the requirements of the professional accounting bodies with Masters programmes in accounting and data analytics. The research identified key themes relevant to the accounting profession that should be explicitly developed and assessed within accounting education for Big Data and data analytics at both university and professional levels. Further analysis of the in-depth curricula, as opposed to the explicitly stated topic coverage, could add to this body of research. Practical implications This paper considers the potential combined role of professional qualification examinations and master’s degrees in skills provision for future practitioners in accounting and data analysis. This can be used to identify the areas in which accounting education can be further enhanced by focus or explicit mention of skills that are both developed and assessed within these programmes. Social implications The paper considers the interaction between academic and professional practice in the areas of accounting education, highlighting skills and areas for development for students currently considering accounting education and data analytics. Originality/value While current literature focusses on integrating data analysis into existing accounting and finance curricula, this paper considers the role of professional qualification examinations with Masters degrees as skills provision for future practitioners in accounting and data analysis.

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