In matthewhirschey/bespokelearnr: Bespoke data science learning

Emerging field of Data Science

url <- "https://images.squarespace-cdn.com/content/v1/5150aec6e4b0e340ec52710a/1364352051365-HZAS3CLBF7ABLE3F5OBY/ke17ZwdGBToddI8pDm48kB2M2-8_3EzuSSXvzQBRsa1Zw-zPPgdn4jUwVcJE1ZvWQUxwkmyExglNqGp0IvTJZUJFbgE-7XRK3dMEBRBhUpxPe_8B-x4gq2tfVez1FwLYYZXud0o-3jV-FAs7tmkMHY-a7GzQZKbHRGZboWC-fOc/Data_Science_VD.png?format=1500w"
knitr::include_graphics(url)

http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram

Venn Diagram of Data Science v2.0

url <- "https://3.bp.blogspot.com/-bvQxcwfqATQ/V-E_uTBc4VI/AAAAAAAAMGQ/Qa1Ntef-rs0E-mWx5pkVu-CPlREdvD0TwCLcB/s1600/VennDiagram2.png"
knitr::include_graphics(url)

Joel Grus via KDnuggets

Overall goal is Knowledge Generation

url <- "https://upload.wikimedia.org/wikipedia/commons/0/06/DIKW_Pyramid.svg"
knitr::include_graphics(url)

https://en.wikipedia.org/wiki/DIKW_pyramid

Several Approaches to Knowledge Generation {.build}

• New tools allow data interrogation more easily than ever before

tweet <- twitterwidget('1125268670324695041')

r tweet

World's most popular programming languages

excel <- tibble(
  name = c("Excel", "Java", "C", "C++", "Python"), 
  num = c(100000000, 9000000, 6000000, 4000000, 3000000)
)
excel_plot <- ggplot(excel) +
  geom_col(aes(x = fct_rev(fct_reorder(name, num)), y = num), fill = "navy") +
  labs(x = "", y = "Number of Users (Million)") +
  scale_y_continuous(labels = c("0", "25", "50", "75", "100")) +
  theme_minimal()
excel_plot
#need to add source

Your choice in programming language {.build}

• Data scientists and programmers have strong opinions about the differences in languages

• The focus here will be on the R programming language

"It's not important which language you choose, but rather what you can do with it"

matthewhirschey/bespokelearnr documentation built on Oct. 11, 2020, 12:57 a.m.