’
Demand for Statisticians and Data Scientists
code
library(readxl)
library(ggplot2)
library(forcats)
library(patchwork)
job <- read_excel("job posting.xlsx")
job |>
ggplot(aes(x = fct_reorder(Country, Stat),
y = Stat,
fill = Country)) +
geom_col() +
geom_text(vjust = 0.5,
hjust = 1.2,
size = 4,
label = job$Stat) +
labs(x = "Country",
y = " ") +
coord_flip() +
theme_classic() +
theme(legend.position = "none") +
ggtitle("Number of Job Postings for Statisticians as of Sept 2022")
code
library(readxl)
library(ggplot2)
library(forcats)
library(patchwork)
job <- read_excel("job posting.xlsx")
job |>
ggplot(aes(x = fct_reorder(Country, DS),
y = DS,
fill = Country)) +
geom_col() +
geom_text(vjust = 0.5,
hjust = 1.2,
size = 4,
label = job$DS) +
labs(x = "Country",
y = " ") +
coord_flip() +
theme_classic() +
theme(legend.position = "none") +
ggtitle("Number of Job Postings for Data Scientists as of Sept 2022")
Skills Set Needed for a Successful Statistician
Mathematics and Statistics
- Probability & statistical inference
- Regression analysis & multivariate analysis
- Time series analysis
- Linear algebra & Calculus
Software
- R/R Studio
- Python
- SAS
- Stata
- Dissect complex problems
- Translate a research or business question into a statistical question
- Discern between good and bad statistics
- Critically assess validity and reliability of statistical methods and results
- Ability to translate statistical findings into clear, actionable recommendations
- Able to present analyses results in layman’s terms
- Has a strong foundation in data visualization
- Able to write a clear and structured reports of statistical analysis and results
- Able to tell a story behind the data and highlight the relevance and implications of findings to decision-making
Data Visualization
code
library(gapminder)
library(gganimate)
ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, colour = country)) +
geom_point(alpha = 0.7, show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
scale_x_log10() +
facet_wrap(~continent) +
labs(title = 'Year: {frame_time}', x = '', y = 'Life expectancy') +
ggtitle("GDP per capita of Countries: 1952-2007") +
transition_time(year) +
ease_aes('linear')
