A Mixed-Method Approach to Investigating Difficulty in Data Science Education

The purpose of this study was to define a methodology to identify any disconnect between students and instructors in data science classrooms through analyzing qualitative data. A combined qualitative and quantitative approach was used for analysis of survey data from students, faculty/instructors, and teaching assistants across three institutions. Using a manual content analysis paired with a TF-IDF analysis, researchers were able to pull out frequently used terms within responses and encode them into categories and subcategories. Trends were identified from these categories and subcategories to examine general areas of disconnect within the data science classroom. Additionally, a quality analysis was run to determine the effectiveness of the phrasing of the questions posed during the survey. As a whole, the methods used throughout this research process provide direction for researchers in interpretation and analysis of the survey data in an efficient and time-sensitive manner. Furthermore, it allows researchers to analyze the quality of responses to give insight towards rephrasing of survey questions in future analyses. Although the research was applied to data science classrooms, this method has the potential to be applied into other fields and areas of study when performed with coordination between a field expert and a data scientist

Network Graph Categorization Based on Features

Having a large collection of varied network graph data is significant for research findings. We have revealed that complex networks of their respective categories (cheminformatic, ecology, and infrastructure network graphs) have distinct similar structural properties amongst themselves. The goal of this project is to be able to more effectively and accurately categorize different graph networks through various machine learning algorithms (logistic regression, lasso regression, linear SVC, decision tree, and random forest and obtained the most important feature of the graphs) based on underlying features within each respective category. In order to achieve a more accurate categorization, more graph features are being included in the machine learning algorithm. The tools we used are C++ for calculating features and python for parsing and organizing features

Effects of On-Field Performance on MLB Fan Attendance

Major League Baseball (MLB) is big business with pre-pandemic revenues exceeding $10 billion. The impact of fan attendance on revenues was evidenced during COVID when revenues dropped below$4 billion. The objective of this research was to explore factors that influence MLB attendance. More specifically, this research looked at what teams were doing on the field and how performance in certain categories influenced fan attendance. A regression analysis, followed by backwards selection, was conducted to develop the simplest model that could be used to explain overall attendance. Various team statistics were evaluated during the model building process. Contrary to popular belief, the number of home runs was not significant in predicting fan attendance. Team data between the years 2015-2019 was used due to irregular attendance caused by the pandemic. The results from this research may be used by league owners to increase revenues but more importantly to increase fan satisfaction. Future research should explore other factors that contribute to fan attendance with the goal of building a model that may be used for predictive purposes

A Mixed-Method Approach to Investigating Difficulty in Data Science Education

The purpose of this study was to define a methodology to identify any disconnect between students and instructors in data science classrooms through analyzing qualitative data. A combined qualitative and quantitative approach was used for analysis of survey data from students, faculty/instructors, and teaching assistants across three institutions. Using a manual content analysis paired with a TF-IDF analysis, researchers were able to pull out frequently used terms within responses and encode them into categories and subcategories. Trends were identified from these categories and subcategories to examine general areas of disconnect within the data science classroom. Additionally, a quality analysis was run to determine the effectiveness of the phrasing of the questions posed during the survey. As a whole, the methods used throughout this research process provide direction for researchers in interpretation and analysis of the survey data in an efficient and time-sensitive manner. Furthermore, it allows researchers to analyze the quality of responses to give insight towards rephrasing of survey questions in future analyses. Although the research was applied to data science classrooms, this method has the potential to be applied into other fields and areas of study when performed with coordination between a field expert and a data scientist