Introduction to me!

I grew up in Mason, OH, and currently live in Lebanon, OH. I completed my undergraduate degree in Business at the University of Cincinnati, where I also minored in Fashion, French, and Film Studies. During my time at UC, I had the opportunity to study abroad four times, which further fueled my passion for travel and cultural exploration.

As I continue to grow professionally, I’m eager to enhance my analytical skills through this course, as I believe these skills will be crucial for advancing my career. I’m excited to leverage my diverse background and experience to bring a unique perspective to my future career.

Hi, it’s me
Hi, it’s me

Academic Background

Professional Background

I’m currently pursuing my MBA with a Data Analytics certificate, focusing on sharpening my analytical skills. This semester, I am a full-time student, dedicated to expanding my knowledge and expertise in data analytics.

My professional background spans Marketing, Sales, and Analytics, with hands-on experience at Kroger, Lexis Nexis, and 84.51. Working in these dynamic environments has given me a solid foundation in data-driven decision-making, and I’m eager to combine my business acumen with advanced data skills to drive impactful insights and solutions.

Experience with R

Although I have no prior experience with R Programming, I’m eager to dive in and learn. Last semester, I took Statistical Computing, where I gained experience with Python, and I’ve found that there are many similarities between Python and R. Through my coursework, I’ve been exposed to coding concepts more than I initially anticipated, and I’m excited to continue building on that foundation. I’m looking forward to taking a deeper dive into R in this course, and I’m confident that the skills I develop here will be invaluable for advancing my career.

Getting Saucy with R 

library(printr)
## Registered S3 method overwritten by 'printr':
##   method                from     
##   knit_print.data.frame rmarkdown
head(iris)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
5.1 3.5 1.4 0.2 setosa
4.9 3.0 1.4 0.2 setosa
4.7 3.2 1.3 0.2 setosa
4.6 3.1 1.5 0.2 setosa
5.0 3.6 1.4 0.2 setosa
5.4 3.9 1.7 0.4 setosa 
x <- rnorm(100)
y <- 2 * x + rnorm(100)

cor(x, y)
## [1] 0.8761144
## [1] "Testing this out, hiding the code and showing the results."