Introduction to Me!

Hi! I’m Francisca Charles, a Senior Data Scientist currently based in Marysville, Ohio. In my role, I focus on creating and analyzing leads for marketing campaigns, using data to help teams better understand and reach the right customers.

I’m currently pursuing my Master’s degree in Business Analytics at the University of Cincinnati, which has been a great opportunity to strengthen my technical skills and deepen my understanding of applied analytics.

On a more personal note, I was born in Santiago, Chile, and spent most of my childhood and early adult life in New York. About 12 years ago, I moved to Ohio for work, and it has since become home. My background and experiences across different places have shaped how I approach problem-solving, communication, and collaboration in my work.

Here’s a photo so you can put a face with the name:

Academic Background

Professional Background

I’m currently a Senior Data Scientist at Huntington National Bank, where my primary focus is building and analyzing leads for marketing campaigns. In this role, I partner closely with business and marketing teams to use data to support targeting strategies and measure campaign effectiveness.

My day-to-day work includes:

Previously. I worked as a Fraud Analyst at Synchrony Financial, where I used SQL and SAS to analyze large transactional datasets and support fraud detection and risk mitigation efforts.

Experience with R

I would currently describe myself as a newbie in R. This course represents my first formal expereince working with R, and my goal is to build a strong foundation in data wrangling, exploration, and reproducible analysis.

While most of my prior analytics work has been done using other tools, I’m excited to learn how R and the tidyverse support efficient data manipulation and reporting. Through this class, I hope to become more comfortable reading R code, writing my own scripts, and using R Markdown to clearly communicate analytical results.

Experience with Other Analytic Software

The softwares I most commonly use are:

Favorite R Resource

One resource I’ve found useful while learning R Markdown is this site:

Optional Marketing Analytics Example

Example Equation

A common metric used in marketing analytics is conversion rate, which measures how many leads convert relative to total impressions:

\[ Conversion\ Rate = \frac{Conversions}{Total\ Impressions} \]

💻 Example R Code Chunk

Below is a simple example of creating and analyzing a marketing-style dataset in R:

# Create a simple marketing dataset
marketing_data <- data.frame(
  impressions = c(1000, 1500, 1200, 1800, 1600),
  conversions = c(45, 60, 50, 80, 70)
)

# Calculate conversion rates
marketing_data$conversion_rate <- 
  marketing_data$conversions / marketing_data$impressions

# View the data
marketing_data
##   impressions conversions conversion_rate
## 1        1000          45      0.04500000
## 2        1500          60      0.04000000
## 3        1200          50      0.04166667
## 4        1800          80      0.04444444
## 5        1600          70      0.04375000
# Calculate the average conversion rate
mean(marketing_data$conversion_rate)
## [1] 0.04297222