Public Sector Practitioner | Researcher | R Programmer | SCAS Vice President | SSTIG Officer | ASPA PA Times Columnist | Data Science-Machine Learning-Program Consultant
April Heyward is a Public Sector Practitioner, Researcher, R Programmer, SCAS Vice President, SSTIG Officer, ASPA PA Times Columnist, and Consultant in the areas of Data Science, Machine Learning, and Academic and STEM Programs. 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. Her research and teaching interests are Computational Social Science, Data Science, Decision Science, E-Government, Machine Learning, Public Administration, Public Policy, Research Methods, Science Policy, 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 on the left hand side of the website.
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
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
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 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