# Resource Library

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• ### Cartoon: Math Concert

A cartoon that can be used to discuss the expression for a simple linear equation (Y=mx+b). The cartoon was drawn by American cartoonist Jon Carter in 2013.

• ### Cartoon: Novelty Chart Hat

A humorous cartoon to initiate a conversation about time series plots. The cartoon was drawn by American cartoonist Jon Carter in 2017.

• ### Cartoon: Prison Data Projects

A humorous cartoon to initiate a conversation about the appropriate graphic to use in printing different types of data. In the cartoon, the prisoner objects to only being allowed to use bar graphs in prison data projects. Looking at the graphic on the poster on the wall, a teacher might ask their students what type of data would be appropriate for what the bar graph is trying to show. The cartoon was drawn by American cartoonist Jon Carter in 2017.

• ### Cartoon: Repair Budget Graph

A humorous cartoon to initiate a conversation about interpreting a time series plot (e.g. discussing trend versus random components). The cartoon was drawn by American cartoonist Jon Carter in 2014.

• ### Cartoon: Street Corner Analytics

A humorous cartoon to initiate a conversation about reasons for low response rates. The cartoon was drawn by American cartoonist Jon Carter in 2013.

• ### Cartoon: Tax Software Sizzle

A humorous cartoon to initiate a conversation about the importance of using graphics for a purpose  in order to show important features of data and not just to add sizzle. The cartoon was drawn by American cartoonist Jon Carter in 2015.

• ### Cartoon: Visual Data Speaking

A humorous cartoon to initiate a conversation about how graphs are an efficient "language" for describing data. The cartoon is drawn by American cartoonist Jon Carter in 2015.

• ### Cartoon: Censored Data

A humorous cartoon to initiate a conversation about censored data situations such as those seen with survival data. The cartoon is drawn by American cartoonist Jon Carter in 2015.

• ### Video: The Song of the Multi-Armed Bandit

A song designed to assist in teaching the basics of Multi-Armed Bandits, which is a type of machine learning algorithm and is the foundation for many recommender systems. These algorithms spend some part of the time exploiting choices (arms) that they know are good while exploring new choices.  The song (music and lyrics) was written in 2021 by Cynthia Rudin from Duke University and was part of a set of three data science oriented songs that won the grand prize in the 2023 A-mu-sing competition.  The lyrics are full of double entendres so that the whole song has another meaning where the bandit could be someone who just takes advantage of other people! The composer mentions these examples of lines with important meanings:
"explore/exploit" - the fundamental topic in MAB!
"No regrets" - the job of the bandit is to minimize the regret throughout the game for choosing a suboptimal arm
"I keep score" - I keep track of the regrets for all the turns in the game
"without thinking too hard,"  - MAB algorithms typically don't require much computation
"no context, there to use," - This particular bandit isn't a contextual bandit, it doesn't have feature vectors
"uncertainty drove this ride." - rewards are probabilistic
"I always win my game"  - asymptotically the bandit always finds the best arm
"help you, decide without the AB testing you might do" - Bandits are an alternative to massive AB testing of all pairs of arms
"Never, keeping anyone, always looking around and around" - There's always some probability of exploration throughout the play of the bandit algorithm

• ### Video: The Song of the Multi-Armed Bandit

A music video designed to assist in teaching the basics of Multi-Armed Bandits, which is a type of machine learning algorithm and is the foundation for many recommender systems. These algorithms spend some part of the time exploiting choices (arms) that they know are good while exploring new choices (think of an ad company choosing an advertisement they know is good, versus exploring how good a new advertisement is). The music and lyrics were written by Cynthia Rudin of Duke University and was one of three data Science songs that won the grand prize and first in the song category for the 2023 A-mu-sing competition.

The lyrics are full of double entendres so that the whole song has another meaning where the bandit could be someone who just takes advantage of other people! The author provides these examples of some lines with important meanings:
"explore/exploit" - the fundamental topic in MAB!
"No regrets" - the job of the bandit is to minimize the regret throughout the game for choosing a suboptimal arm
"I keep score" - I keep track of the regrets for all the turns in the game
"without thinking too hard,"  - MAB algorithms typically don't require much computation
"no context, there to use," - This particular bandit isn't a contextual bandit, it doesn't have feature vectors
"uncertainty drove this ride." - rewards are probabilistic
"I always win my game"  - asymptotically the bandit always finds the best arm
"help you, decide without the AB testing you might do" - Bandits are an alternative to massive AB testing of all pairs of arms
"Never, keeping anyone, always looking around and around" - There's always some probability of exploration throughout the play of the bandit algorithm