My sister is a Pharmacist. My wife is an Environmental Scientist / Project Manager. They’re both interested in Public Health, though they work in very different environments. Then there’s me: which box do you check for Revenue Management Analyst or Data Scientist?

To try and define my role, I compared three defined options: Financial Analysts, Business Intelligence Analysts and Industrial Engineers. I felt these might be most representative of what I do.

Note: The information used in this analysis is from O-Net Resource Center. Per their site, “The O*NET database contains a rich set of variables that describe work and worker characteristics, including skill requirements."

So, let’s use a radar chart to examine what these three occupations look like.

# Install and load needed libraries
install.packages("radarchart")
library(radarchart)

# Obtain and load data
# Source:  https://www.onetcenter.org/database.html?p=3
# Knowledge download = Knowledge.xlsx
df3 <- read.csv(file="Knowledge3.csv", header=TRUE, sep=",")

# Create radar chart
chartJSRadar(df3, maxScale=5)

Based on the above, I’ll classify myself as a BI Analyst when comparing myself to my sister and wife. For this next comparison, let’s look at skills importance instead of knowledge.

# Obtain and load data
# Source:  https://www.onetcenter.org/database.html?p=3
df4 <- read.csv(file="Skills1.csv", header=TRUE, sep=",")

# Create radar chart
chartJSRadar(df4,maxScale=5,main="Skills Importance",responsive = TRUE,showToolTipLabel = TRUE)

There are, indeed, some overlaps across all three professions. This helps to explain why we have overlap in professional topics. Critical thinking and complex problem solving are high for all three fields. There’s great disparity when it comes to science, however. In total, this all seems to make sense.

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