During the global COVID-19 pandemic in 2020, many universities shifted their focus to hosting classes and events online for their student population in order to keep them engaged. The present study investigated whether an association exists between student engagement (an individual’s engagement with class and campus) and resilience. A single-shot survey was administered to 200 participants currently enrolled as undergraduate students at Arizona State University. A multiple regression analysis and Pearson correlations were calculated. A moderate, significant correlation was found between student engagement (total score) and resilience. A significant correlation was found between cognitive engagement (student’s approach and understanding of his learning) and resilience and between valuing and resilience. Contrary to expectations, participation was not associated with resilience. Potential explanations for these results were explored and practical applications for the university were discussed.

We attempted to apply a novel approach to stock market predictions. The Logistic Regression machine learning algorithm (Joseph Berkson) was applied to analyze news article headlines as represented by a bag-of-words (tri-gram and single-gram) representation in an attempt to predict the trends of stock prices based on the Dow Jones Industrial Average. The results showed that a tri-gram bag led to a 49% trend accuracy, a 1% increase when compared to the single-gram representation’s accuracy of 48%.

The influx of readily available sports data has transformed the landscape of recruitment analysis conducted in European soccer leagues. Clubs now have access to a repository of information that helps to monitor the status of current players and filter those they wish to recruit. Supplemented by extensive financial backing, the teams in the English Premier League have shifted from a local, more traditional approach to a focus on the acquisition of players in international markets. This paper analyzes the rapid effects of implementing a data-driven approach to recruitment and argues that the dominance of Liverpool in the EPL from 2017 to 2022 has stemmed from a superior focus in this data-driven recruitment compared to other clubs in the league, specifically Manchester United. Other teams have recently shifted their structures to model the modern, fast flow of data that the two European super clubs manage each season yet consistently fail to match either. Furthermore, this project establishes the feasible prospect of clubs prioritizing their staffing for data over other departments, including players.

The influx of readily available sports data has transformed the landscape of recruitment analysis conducted in European soccer leagues. Clubs now have access to a repository of information that helps to monitor the status of current players and filter those they wish to recruit. Supplemented by extensive financial backing, the teams in the English Premier League have shifted from a local, more traditional approach to a focus on the acquisition of players in international markets. This paper analyzes the rapid effects of implementing a data-driven approach to recruitment and argues that the dominance of Liverpool in the EPL from 2017 to 2022 has stemmed from a superior focus in this data-driven recruitment compared to other clubs in the league, specifically Manchester United. Other teams have recently shifted their structures to model the modern, fast flow of data that the two European super clubs manage each season yet consistently fail to match either. Furthermore, this project establishes the feasible prospect of clubs prioritizing their staffing for data over other departments, including players.