Matching Items (7)
Description
This project analyzes the tweets from the 2016 US Presidential Candidates' personal Twitter accounts. The goal is to define distinct patterns and differences between candidates and parties use of social media as a platform. The data spans the period of September 2015 to March 2016, which was during the primary races for the Republicans and Democrats. The overall purpose of this project is to contribute to finding new ways of driving value from social media, in particular Twitter.
ContributorsMortimer, Schuyler Kenneth (Author) / Simon, Alan (Thesis director) / Mousavi, Seyedreza (Committee member) / Department of Information Systems (Contributor) / Department of Supply Chain Management (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
With growing levels of income inequality in the United States, it remains as important as ever to ensure indispensable public services are readily available to all members of society. This paper investigates four forms of public services (schools, libraries, fire stations, and police stations), first by researching the background of these services and their relation to poverty, and then by conducting geospatial and regression analysis. The author uses Esri's ArcGIS Pro software to quantify the proximity to public services from urban American neighborhoods (census tracts in the cities of Phoenix and Chicago). Afterwards, the measures indicating proximity are compared to the socioeconomic statuses of neighborhoods using regression analysis. The results indicate that pure proximity to these four services is not necessarily correlated to socioeconomic status. While the paper does uncover some correlations, such as a relationship between school quality and socioeconomic status, the majority of the findings negate the author's hypothesis and show that, in Phoenix and Chicago, there is not much discrepancy between neighborhoods and the extent to which they are able to access vital government-funded services.
ContributorsNorbury, Adam Charles (Author) / Simon, Alan (Thesis director) / Simon, Phil (Committee member) / Department of Information Systems (Contributor) / Department of English (Contributor) / Department of Economics (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Abstract: The Ultimate Fighting Championship or UFC as it is commonly known, was founded in 1993 and has quickly built itself into the world's foremost authority on all things MMA (mixed martial arts) related. With pay-per-view and cable television deals in hand, the UFC has become a huge competitor in the sports market, rivaling the popularity of boxing for almost a decade. As with most other sports, the UFC has seen an influx of various analytics and data science over the past five to seven years. We see this revolution in football with the broadcast first down markers, basketball with tracking player movement, and baseball with locating pitches for strikes and balls, and now the UFC has partnered with statistics company Fightmetric, to provide in-depth statistical analysis of its fights. ESPN has their win probability metrics, and statistical predictive modeling has begun to spread throughout sports. All these stats were made to showcase the information about a fighter that one wouldn't typically know, giving insight into how the fight might go. But, can these fights be predicted? Based off of the research of prior individuals and combining the thought processes of relevant research into other sports leagues, I sought to use the arsenal of statistical analyses done by Fightmetric, along with the official UFC fighter database to answer the question of whether UFC fights could be predicted. Specifically, by using only data that would be known about a fighter prior to stepping into the cage, could I predict with any degree of certainty who was going to win the fight?
ContributorsMoorman, Taylor D. (Author) / Simon, Alan (Thesis director) / Simon, Phil (Committee member) / W.P. Carey School of Business (Contributor) / Department of Information Systems (Contributor) / Department of Management and Entrepreneurship (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
League of Legends is a popular multiplayer online battle arena (MOBA) developed by Riot Games. Each team consists of 5 players who each control a single character (champion) which they select at the beginning of each game. In order to win the match, a team has to destroy the Nexus (the central structure) in the opponent's base. League of Legends has grown rapidly since its release in 2009 and has over 70 million registered players. Several community websites have been created that track the performance of players and show detailed statistics for just about every aspect of the game. This project focuses on exploring the applicability of predictive analytics within League of Legends, by predicting the outcome of any given ranked match at the start of the game. It resulted in a model with accuracy of 58% using decision trees. An additional contribution of the project is a solution to a data collection anomaly that has biased previous studies.
ContributorsNeumann, Alexander (Author) / Clark, Joseph (Thesis director) / Simon, Alan (Committee member) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
Description
League of Legends is a Multiplayer Online Battle Arena (MOBA) game. MOBA games are generally formatted where two teams of five, each player controlling a character (champion), will try to take each other's base as quickly as possible. Currently, with about 70 million, League of Legends is number one in the digital entertainment industry with $1.63 billion dollars of revenue in year 2015. This research analysis scopes in on the niche of the "Jungler" role between different tiers of player in League of Legends. I uncovered differences in player strategy that may explain the achievement of high rank using data aggregation through Riot Games' API, data slicing with time-sensitive data, random sampling, clustering by tiers, graphical techniques to display the cluster, distribution analysis and finally, a comprehensive factor analysis on the data's implications.
ContributorsPoon, Alex (Author) / Clark, Joseph (Thesis director) / Simon, Alan (Committee member) / Department of Information Systems (Contributor) / Department of Management (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Sports analytics refers to the implementation of data science and analytics techniques within the sports industry. Several sports analysts and team managers have utilized analytical tools to boost overall team and player performance, often through the analysis of historical data. One of the most common techniques employed in sports analytics is that of data mining–the extensive practice of analyzing data in order to extract and deliver insights and findings. Data mining projects are frequently guided with the six-step Cross Industry Standard Process for Data Mining (CRISP-DM) framework. One such sport that has extensively used data science and analytics, and data mining specifically, is that of Formula One (F1). Given the sports’ reliance on technology, race engineers working for F1 constructors often develop statistical models analyzing historical race performance to derive insight of drivers’ success. For the purposes of this project, the perspective of a race engineer working for the F1 constructor McLaren was considered. As the constructor is seeking to gain a competitive advantage for the upcoming F1 season, race performance data concerning previous seasons was collected and analyzed as part of a larger data mining project utilizing the CRISP-DM framework. Statistical models, such as linear regression and random forest, were developed to predict the number of points scored by McLaren racers and the variables most strongly contributed to such scored points. The final results point to specific lap times having to be aimed for as the most important variable in determining the number of points gained, although specific locations also seem prone to McLaren race success. These results in turn will be utilized to develop race strategies for the upcoming season to ensure McLaren has high efficiency against its competitors.
ContributorsImam, Amir (Author) / Simon, Alan (Thesis director) / Sha, Xiqing (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor)
Created2023-05
Description
In recent years, artificial intelligence (AI) has become an increasingly important resource for individuals and corporations. In the health care industry, there is a growing demand for AI and related technologies to assist in everything from surgery to janitorial duties. While there is the usual skepticism surrounding AI, it is important to understand how it works, its benefits, and its risk. Medically speaking, AI has the potential to aid physicians in diagnosis and treatment recommendations. The technology, however, presents issues surrounding data privacy as well as embedded biases in the algorithms used for intelligent systems. Mitigating these potential risks will be important to the successful implementation of AI in a health care setting. As artificial intelligence becomes more prominent in this space, there is a valid fear that it will result in severe job displacement and force many people out of their fields. This transition towards a more automated hospital will require many medical personnel to learn new skills and move towards jobs that require a more significant amount of empathy to thrive in the new economy. For decades, people have been worried about the effects of more automation and machinery, but we have seen that where some jobs are replaced with technology, other jobs are created. The findings of this thesis show that while AI is likely to replace some human workers, there is minimal reason to worry about complete job displacement in the near future. Long-term, the significance of AI in job replacement is more of an unknown. However, with knowledge about how artificial intelligence could be impacting us now, patients, physicians, and hospitals can better prepare and understand the impact that AI will have on each of them specifically.
ContributorsMcSpadden, Lana (Author) / Simon, Alan (Thesis director) / Ostrom, Amy (Committee member) / Barrett, The Honors College (Contributor) / Business Data Analytics (Contributor)
Created2022-05