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Margaret Marchant; Ethan Eliason – Journal of Education for Business, 2024
Undergraduate economics programs prepare students for future careers by developing competency working with data, or "data literacy." Our research examined the data literacy components of undergraduate economics programs at R1 and R2 universities in the United States (N = 190). We developed a protocol with core data skills and coded…
Descriptors: Undergraduate Students, Economics Education, Data Collection, Data Interpretation
Callanan, Gerard A.; Perri, David F.; Tomkowicz, Sandra M. – Journal of Education for Business, 2018
The authors present a pedagogical primer on the highly controversial business strategies of data mining and automated prediction. They provide a summary that allows business professors and students the opportunity to better understand the privacy and ethical issues that arise from high-tech, Internet-based organizations implementing programs to…
Descriptors: Automation, Prediction, Discussion (Teaching Technique), Privacy
Ashraf, Rasha – Journal of Education for Business, 2017
This article presents Python codes that can be used to extract data from Securities and Exchange Commission (SEC) filings. The Python program web crawls to obtain URL paths for company filings of required reports, such as Form 10-K. The program then performs a textual analysis and counts the number of occurrences of words in the filing that…
Descriptors: Information Retrieval, Search Engines, Search Strategies, Online Searching
Zhao, Jensen; Zhao, Sherry Y. – Journal of Education for Business, 2016
E-business, e-education, e-government, social media, and mobile services generate and capture trillions of bytes of data every second about customers, suppliers, employees, and other types of data. The growing quantity of big data is an important part of every sector in the global economy. However, there is a significant shortage of business data…
Descriptors: Business Administration Education, Business Schools, Data Analysis, Data Processing

Rosenbaum, Roberta – Journal of Education for Business, 1986
Appropriate strategies for teaching students to interpret and understand quantitative data in marketing, management, accounting, and data processing are described. Accompanying figures illustrate samples of percentage markups, trade discounts, gross earning, gross commissions, accounting entries, balance sheet entries, and percentage problems. (CT)
Descriptors: Accounting, Business Administration, Critical Thinking, Data Processing