As growth of the data analytics field continues, more and more academic departments and professional training programs are offering skill training to potential data analysts. However, not all business domains require their data analysts to possess the same sets of technical skills. Based on over 2,500 data-analyst job advertisements posted on LinkedIn and utilizing subject analysis, cross-tabulation analysis, heat map, Kruskal-Wallis test, and Kappa test, this study examined relationships between various business domains and the technical skills required for data analysts within the respective business domains. Among many findings, the study discovered noteworthy disparities among the business domains concerning their requirements of technical-skill sets for data analysts. The findings of this study offer valuable insights to educators and potential data analysts.

As the data-analysts job market grows, many colleges and universities have started offering a data analytics curriculum. However, there are potential gaps between the skills business organizations expect of data analysts and the skills universities and colleges teach their students. This study collected 2500+ data-analyst job ads posted on LinkedIn and analyzed them using distribution analysis, cross-tabulation analysis, and cluster analysis. Among many findings, this study identified five most essential nontechnical skills and five most essentials areas of technical skills. In addition, of 90+ computer programs business organizations expect data analysts to use, this study identified SQL, Microsoft Excel, Tableau, Python, and Microsoft Power BI to be the five most essential computer programs for potential data analysts to master.

Inducing more and higher-quality answers to questions is essential to sustainable development of Social Question-and-Answer (SQA) websites. Previous research has studied factors affecting question success and user motivation in answering questions, but how a question’s own characteristics affect the question’s answer outcome on SQA websites remains unknown. This study examines the impact of the characteristics of a question, namely readability, emotionality, additional descriptions, and question type, on the question’s answer outcome as measured by number of answers, average answer length, and number of “likes” received by answers to the question. Regression analyses reveal that readability, additional descriptions, and question type have significant impact on multiple measurements of answer outcome, while emotionality only affects the average answer length. This study provides insights to SQA website builders as they instruct users on question construction. It also provides insights to SQA website users on how to induce more and higher-quality answers to their questions.

Relevance judgment plays an extremely significant role in information retrieval. This study investigates the differences between American users and Chinese users in relevance judgment during the information retrieval process. 384 sets of relevance scores with 50 scores in each set were collected from 16 American users and 16 Chinese users as they judged retrieval records from two major search engines based on 24 predefined search tasks from 4 domain categories. Statistical analyses reveal that there are significant differences between American assessors and Chinese assessors in relevance judgments. Significant gender differences also appear within both the American and the Chinese assessor groups. The study also revealed significant interactions among cultures, genders, and subject categories. These findings can enhance the understanding of cultural impact on information retrieval and can assist in the design of effective cross-language information retrieval systems.

This article describes how as internet technology continues to change and improve lives and societies worldwide, effective global information management becomes increasingly critical, and effective Internet information retrieval systems become more and more significant in providing Internet users worldwide with accurate and complete information. Search engine evaluation is an important research field as search engines directly determine the quality of information users' Internet searches. Relevance-decrease pattern/model plays an important role in search engine result evaluation. This research studies effective measurement of search results through investigating relevance-decrease patterns of search results from two popular search engines: Google and Bing. The findings can be applied to relevance-evaluation of search results from other information retrieval systems such as OPAC, can help make search engine evaluations more accurate and sound, and can provide global information management personnel with valuable insights.