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April Heyward is a Public Sector Practitioner, Adjunct Faculty, Public Administration Doctoral Researcher, R Programmer, and ASPA (American Society for Public Administration) PA (Public Administration) Times Columnist. She is the Program Manager for the SC EPSCoR (Established Program to Stimulate Competitive Research) Program which is a science-driven state-based National Science Foundation (NSF) program. She is an Adjunct Faculty in the Master of Public Administration (MPA) Program in the Department of Political Science in the School of Humanities and Social Sciences at the College of Charleston. She is credentialed to teach PUBA 601 Research Methods for Public Administration, PUBA 602 Public Policy, and PUBA 604 Managing Human Resources courses. Her teaching and research interests are Computational Social Science, Data Science, Decision Science, E-Government, Machine Learning, Public Administration, Public Policy, Research Methods, and Social Media Research. April is integrating Data Science and Machine Learning algorithms and employing R programming specifically RStudio into her research, research methods, and non-research projects. Her long-term research focus is how Data Science and Machine Learning can inform and solve complex problems in Public Administration, Public Policy, and the other sub-disciplines of Social Science. April is pursuing her Doctorate in Public Administration in the Department of Political Science in the College of Humanities and Social Science at Valdosta State University. She is in the Dissertation phase of her Doctoral Program. For more information on April Heyward, please visit the website tabs (including the sub tabs) on the left hand side of the website.
Latest Posts
Text Mining in R
Text mining is a machine learning algorithm that I employ in my research and non-research projects. I analyze, model, and visualize text in R with numerous R packages and R functions. Text must be cleaned before the analysis, modeling, and visualization stages. Several steps are employed in the text cleaning process. R has the capacity
R Programming Language
I receive questions all the time about the R programming language. R is one of my happy places and I can spend hours, days, and weeks programming. R is vast and its capabilities are extensive from employing data science, machine learning, neural networks, deep learning, apps development, interactive widgets for websites, etc. This is contrary
Self Motivation for the Doctoral Journey
Sometimes when people hear that I am in a Doctoral program and working full-time simultaneously, they look at me and ask how are you doing this? For those who have completed the Doctoral journey, they get the Doctoral life as there are common experiences that all Doctoral students experience regardless of discipline. For those who
Artificial Intelligence + Social Science
Artificial Intelligence is a fascinating discipline and application that I have been studying over the last year and recently began to investigate adapting and applying to my research projects. The discipline can be segmented into subdisciplines to include Machine Learning (e.g., Supervised Learning, Unsupervised Learning, Semi-Supervised Learning), Neural Networks, Deep Learning, etc. Artificial Intelligence emerged
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