Exploratory data analysis (EDA), data visualization, and visual analytics are essential for understanding and analyzing complex datasets. In this project, we explored these techniques and their applications in data analytics. The case discusses Tableau, a powerful data visualization tool, and Google BigQuery, a cloud-based data warehouse that…

The current study details the development of an undergraduate business analytics course that combines components of both active and experiential learning. The course offering is designed to expose students from different backgrounds to an intermediate-to-advanced level of business analytics. The course is unique in that it was designed to be…

The use of text mining tools can help librarians improve the precision of searches, increase search sensitivity, and translate search strategies across multiple research databases. When combined with the intuitive approaches that librarians commonly use, text mining tools help reduce biases by improving the objectivity, transparency, and…

Attrition is one of the main concerns in distance learning due to the impact on the incomes and institutions reputation. Timely identification of students at risk has high practical value in effective students' retention services. Big Data mining and machine learning methods are applied to manipulate, analyze and predict students' failure,…

Attrition is one of the main concerns in distance learning due to the impact on the incomes and institutions reputation. Timely identification of students at risk has high practical value in effective students' retention services. Big Data mining and machine learning methods are applied to manipulate, analyze, and predict students' failure,…

Data is a powerful tool for the healthcare industry to use for managing, analyzing, and reporting on critical events in the field. The analysis of broad, salient data files aids healthcare businesses in uncovering hidden patterns, market trends, and customer preferences; these details may then be used to improve the quality and delivery of care to…

Best practices in statistics and data science courses include the use of real and relevant data as well as teaching the entire data science cycle starting with importing data. A rich source of real and current data is the web, where data are often presented and stored in a structure that needs some wrangling and transforming before they can be…

Despite the recent and ongoing progress in using text-mining tools to automatically analyze large text corpora, there remains significant potential to facilitate the study of social action in social science research. In this context, particularly the disambiguation (who is referred to in a text?) and specification (which demographic…

Experiential learning opportunities have been proven effective in teaching applied and complex subjects such as business analytics. Current business analytics pedagogy tends to focus heavily on the modeling phase with students often lacking a comprehensive understanding of the entire analytics process including dealing with real-life data that are…

There is an increasing interest and growing practice in Citizen Science (CS) that goes along with the usage of websites for communication as well as for capturing and processing data and materials. From an educational perspective, it is expected that by integrating information about CS in a formal educational setting, it will inspire teachers to…

It is recommended that teacher-scholars of data science adopt reproducible workflows in their research as scholars and teach reproducible workflows to their students. In this article, we propose a third dimension to reproducibility practices and recommend that regardless of whether they teach reproducibility in their courses or not, data science…

Launching biomedical innovations based on clinical demands instead of translating basic research findings to practice reduces the risk that the results will not fit the clinical routine. To realize this type of innovation, a meta-analysis of the body of research is necessary to reveal demand-matching concepts. However, both the data deluge and the…

This paper describes two classes of advanced data mining methods that can obtain actionable insight from cooperative education data: text mining of job descriptions and graph mining of job interview data. While these methods are not new in general, they have not been widely used in co-operative education research. A technical overview of each…

Analyses of student data in post-secondary education should be sensitive to the fact that there are many different topics of study. These different areas will interest different kinds of students, and entail different experiences and learning activities. However, it can be challenging to identify the distinct academic themes that students might…

Learning analytics dashboards commonly visualize data about students with the aim of helping students and educators understand and make informed decisions about the learning process. To assist with making sense of complex and multidimensional data, many learning analytics systems and dashboards have relied strongly on AI algorithms based on…