As a Business Intelligence Developer for North Carolina’s State Employee Health Plan, I spend my days writing SAS code and preparing data for the business users. I’ve recently discovered that I also have a passion for teaching. I know that learning statistics can, and should, be fun. It is, after all, the mathematics of games and gambling. I am always working to communicate this sense of fun to my students.
I was born in Baton Rouge, LA and lived for many years in New Orleans. I moved to Durham in 1999 and currently live in Creedmoor, NC, about 25 miles North of Raleigh. When I’m not in the office or doing schoolwork, you can usually find me playing guitar in a bluegrass jam somewhere, or driving my roadster on twisty mountain roads.
Master of Statistics, 2018
North Carolina State University
BA in Mathematical Sciences, 2015
University of Illinois at Springfield
This course is a study of basic descriptive and inferential statistics. Topics include types of data, sampling techniques, measures of central tendency and dispersion, elementary probability, discrete and continuous probability distributions, sampling distributions, hypothesis testing, confidence intervals, and simple linear regression.
Students are expected to use a wide and complex set of computer tools and systems. A purpose of this course is to build upon their existing knowledge and help ensure students are proficient in common computer systems and with a skill set to solve a wide variety of data analysis problems. Using Microsoft Excel and R software along with their advanced features students will expand their understanding of computers and software while being equipped to solve large and dynamic data sets.
This course introduces basic concepts and applications of analytics using Excel. Topics include an overview of the analytical process and the role of the analyst, applied descriptive statistics, and exploratory data analysis.
This course introduces key concepts in data visualization and reporting, using Tableau software. Topics include concepts and methods used in graphical representation of data, exploration and reporting of data, and basic linear regression methods.
This course introduces SAS for analytics. Topics include utilization of analytical and statistical software packages for data management, data visualization, and exploratory data analysis. Recent Syllabus
This course covers applications of SAS for data management and reporting. Topics include data management, data preprocessing, and modeling including linear and logistic regression analysis using programming tools. Upon completion, students should be able to process data and generate reports that support business decision-making.
Students learn the fundamentals of probability, statistics, and their applications in business decision making.