A Case Study of Basic Data Science Education using Public Big Data Collection and Spreadsheets for Teacher Education

The chapter discusses how Financial Services organizations can take advantage of Big Data analysis for disruptive innovation through examination of a case study in the financial services industry. Popular tools for Big Data Analysis are discussed and the challenges of big data are explored as well as how these challenges can be met. The work of Hayes-Roth in Valued Information at the Right Time (VIRT) and how it applies to the case study is examined. Boyd's model of Observe, Orient, Decide, and Act (OODA) is explained in relation to disruptive innovation in financial services. Future trends in big data analysis in the financial services domain are explored.

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Big Data Techniques for Supporting Official Statistics

Web Services ◽

10.4018/978-1-5225-7501-6.ch040 ◽

2019 ◽

pp. 728-744 ◽

Cited By ~ 1

Author(s):

Antonino Virgillito ◽

Federico Polidoro

Keyword(s):

Big Data ◽

Data Collection ◽

Data Science ◽

Official Statistics ◽

The Core ◽

Web Scraping ◽

Collection Process ◽

Data Collection Process ◽

Data Source ◽

Use Of Internet

Following the advent of Big Data, statistical offices have been largely exploring the use of Internet as data source for modernizing their data collection process. Particularly, prices are collected online in several statistical institutes through a technique known as web scraping. The objective of the chapter is to discuss the challenges of web scraping for setting up a continuous data collection process, exploring and classifying the more widespread techniques and presenting how they are used in practical cases. The main technical notions behind web scraping are presented and explained in order to give also to readers with no background in IT the sufficient elements to fully comprehend scraping techniques, promoting the building of mixed skills that is at the core of the spirit of modern data science. Challenges for official statistics deriving from the use of web scraping are briefly sketched. Finally, research ideas for overcoming the limitations of current techniques are presented and discussed.

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How Should Data Science Education Be?

International Journal of Energy Optimization and Engineering ◽

10.4018/ijeoe.2020040103 ◽

2020 ◽

Vol 9 (2) ◽

pp. 25-36

Author(s):

Necmi Gürsakal ◽

Ecem Ozkan ◽

Fırat Melih Yılmaz ◽

Deniz Oktay

Keyword(s):

Machine Learning ◽

Big Data ◽

Science Education ◽

Data Science ◽

Doctoral Programs ◽

Time Data ◽

High Demand ◽

The Core ◽

The World ◽

The Subject

The interest in data science is increasing in recent years. Data science, including mathematics, statistics, big data, machine learning, and deep learning, can be considered as the intersection of statistics, mathematics and computer science. Although the debate continues about the core area of data science, the subject is a huge hit. Universities have a high demand for data science. They are trying to live up to this demand by opening postgraduate and doctoral programs. Since the subject is a new field, there are significant differences between the programs given by universities in data science. Besides, since the subject is close to statistics, most of the time, data science programs are opened in the statistics departments, and this also causes differences between the programs. In this article, we will summarize the data science education developments in the world and in Turkey specifically and how data science education should be at the graduate level.

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A Project-Based Case Study of Data Science Education

Data Science Journal ◽

10.5334/dsj-2016-005 ◽

2016 ◽

Vol 15 ◽

Keyword(s):

Science Education ◽

Data Science

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Data Science and Big Data Analytics in Financial Services

Handbook of Research on Driving Competitive Advantage through Sustainable, Lean, and Disruptive Innovation - Advances in Business Strategy and Competitive Advantage ◽

10.4018/978-1-5225-0135-0.ch017 ◽

2016 ◽

pp. 396-423 ◽

Cited By ~ 1

Author(s):

Suren Behari ◽

Aileen Cater-Steel ◽

Jeffrey Soar

Keyword(s):

Big Data ◽

Data Analysis ◽

Financial Services ◽

Data Science ◽

Big Data Analytics ◽

Disruptive Innovation ◽

Big Data Analysis ◽

Future Trends ◽

The Right

The chapter discusses how Financial Services organizations can take advantage of Big Data analysis for disruptive innovation through examination of a case study in the financial services industry. Popular tools for Big Data Analysis are discussed and the challenges of big data are explored as well as how these challenges can be met. The work of Hayes-Roth in Valued Information at the Right Time (VIRT) and how it applies to the case study is examined. Boyd's model of Observe, Orient, Decide, and Act (OODA) is explained in relation to disruptive innovation in financial services. Future trends in big data analysis in the financial services domain are explored.

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A Case Study of Beginning Elementary School Teachers’View Changes on the Nature of Science and Science Education through Teacher Education of La main à la pâte Program

Teacher Education Research ◽

10.15812/ter.52.2.201308.215 ◽

2013 ◽

Vol 52 (2) ◽

pp. 215-226

Author(s):

고유경 ◽

Seoung-Hey Paik ◽

Jungin Chung

Keyword(s):

Teacher Education ◽

Elementary School ◽

Science Education ◽

Nature Of Science

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IT SOLUTIONS, PLC.: INTRODUCING A BIG DATA CASE INTO THE ACCOUNTING CURRICULUM

Proceedings of CBU in Economics and Business ◽

10.12955/peb.v2.259 ◽

2021 ◽

Vol 2 ◽

pp. 88-94

Author(s):

Eleonora Stancheva-Todorova ◽

Mirella Dimitrova

Keyword(s):

Big Data ◽

Financial Reporting ◽

Data Science ◽

Stock Exchange ◽

Annual Reports ◽

Business World ◽

Research Perspective ◽

Technological Competences ◽

And Performance

By bridging the accounting and data science domains, this paper introduces an interdisciplinary Big Data case study for accounting students that implements a specific methodology framework. It is supported by clear learning objectives and detailed instructor’s implementation guidance that complement a fascinating scenario, representing a real-world situation in the data-led world of business. The participants’ assignment is to propose a strategy for improving financial position and performance of a particular company by attracting new customers selected among companies, listed on the London Stock Exchange. The data sources of the proposed case study are publicly available and comprise of historical financial and non-financial data, disclosed in companies’ annual reports. By performing their assigned roles under the case study scenario, future graduates will build upon their technological competences as well as raise their awareness on the new roles and job tasks of the future accountant. They will also gain understanding on the new advisory function of the accounting specialists and their responsibilities as management consultants in the data-let business world. From a research perspective, this interdisciplinary work demonstrates how expertise in text mining and financial reporting might be combined for revealing new investment opportunities and enhancing management decisions.

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Big Data Techniques for Supporting Official Statistics

Data Visualization and Statistical Literacy for Open and Big Data - Advances in Data Mining and Database Management ◽

10.4018/978-1-5225-2512-7.ch010 ◽

2017 ◽

pp. 253-273

Author(s):

Antonino Virgillito ◽

Federico Polidoro

Keyword(s):

Big Data ◽

Data Collection ◽

Data Science ◽

Official Statistics ◽

The Core ◽

Web Scraping ◽

Collection Process ◽

Data Collection Process ◽

Data Source ◽

Use Of Internet

Following the advent of Big Data, statistical offices have been largely exploring the use of Internet as data source for modernizing their data collection process. Particularly, prices are collected online in several statistical institutes through a technique known as web scraping. The objective of the chapter is to discuss the challenges of web scraping for setting up a continuous data collection process, exploring and classifying the more widespread techniques and presenting how they are used in practical cases. The main technical notions behind web scraping are presented and explained in order to give also to readers with no background in IT the sufficient elements to fully comprehend scraping techniques, promoting the building of mixed skills that is at the core of the spirit of modern data science. Challenges for official statistics deriving from the use of web scraping are briefly sketched. Finally, research ideas for overcoming the limitations of current techniques are presented and discussed.

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Big Data and Data Science: Opportunities and Challenges of iSchools

Journal of Data and Information Science ◽

10.1515/jdis-2017-0011 ◽

2017 ◽

Vol 2 (3) ◽

pp. 1-18 ◽

Cited By ~ 12

Author(s):

Il-Yeol Song ◽

Yongjun Zhu

Keyword(s):

Big Data ◽

Science Education ◽

Data Science ◽

Computational Thinking ◽

Building Blocks ◽

Digital Transformation ◽

Problem Solving Skills ◽

Science Courses ◽

Big Picture ◽

Eye Opening

AbstractDue to the recent explosion of big data, our society has been rapidly going through digital transformation and entering a new world with numerous eye-opening developments. These new trends impact the society and future jobs, and thus student careers. At the heart of this digital transformation is data science, the discipline that makes sense of big data. With many rapidly emerging digital challenges ahead of us, this article discusses perspectives on iSchools’ opportunities and suggestions in data science education. We argue that iSchools should empower their students with “information computing” disciplines, which we define as the ability to solve problems and create values, information, and knowledge using tools in application domains. As specific approaches to enforcing information computing disciplines in data science education, we suggest the three foci of user-based, tool-based, and application-based. These three foci will serve to differentiate the data science education of iSchools from that of computer science or business schools. We present a layered Data Science Education Framework (DSEF) with building blocks that include the three pillars of data science (people, technology, and data), computational thinking, data-driven paradigms, and data science lifecycles. Data science courses built on the top of this framework should thus be executed with user-based, tool-based, and application-based approaches. This framework will help our students think about data science problems from the big picture perspective and foster appropriate problem-solving skills in conjunction with broad perspectives of data science lifecycles. We hope the DSEF discussed in this article will help fellow iSchools in their design of new data science curricula.

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