https://towardsdatascience.com/no-machine-learning-is-not-just-glorified-statistics-26d3952234e3

### Things to learn list

My friend told me yesterday that he has an ‘ignorance’ book – a book of things he wants to learn more about.

I don’t have a book like that, but I do keep a list of classes I would like to take and a list of things to read.

There is never enough time to learn everything, but there is enough time to learn something, so having a book or a list like that , like an amazon wish list of things I understood better, would be helpful for focusing time and energy.

Right now I have this:

I need to work this into the system.

### Buzzwords

### Streaks — AVC

streaks plus little b blogging.

My son told me he wanted to learn Japanese. I told him to check out our portfolio company Duolingo‘s awesome language learning product of the same name. He told me that he has used Duolingo and likes it, particularly the ability to generate streaks. He told me that once you have a streak going, you really…

via Streaks — AVC

### From MIT TECH review

According to a 2017 study by the Brookings Institution, the Toledo metropolitan area is the most roboticized in the United States, with nine robots per 1,000 workers. There were 702 robots there in 2010. By 2015, there were 2,374. There are more now. In March, another study estimated that the state had lost 671,000 jobs to automation between 1967 and 2014, more than it lost to domestic competition (such as from right-to-work states, which restrict the power of unions) and foreign trade combined.

### Uncertainty Wednesday posts in order

Uncertainty Wednesday: A New Series

Uncertainty Wednesday: Reality, Explanations, Observations Uncertainty Wednesday: Limits on Observations Uncertainty Wednesday: Limits on Observations (Resolution) Uncertainty Wednesday: Limits on Observations (Measurement Error) Uncertainty Wednesday: Limits on Observations (Cost) Uncertainty Wednesday: Limits to Observations (Impact) Uncertainty Wednesday: Explanations Uncertainty Wednesday: Limits on Explanations Uncertainty Wednesday: Limits on Explanations (Turing and Gödel) Uncertainty Wednesday: What About Reality? Uncertainty Wednesday: Our First Example (Zoltar) Uncertainty Wednesday: Zoltar Example (Continued) Uncertainty Wednesday: Zoltar Example (Part 3) Uncertainty Wednesday: Zoltar Example (Part 4) Uncertainty Wednesday: Coin Flipping (Example) Uncertainty Wednesday: States and Signals Uncertainty Wednesday: Probability Uncertainty Wednesday: Probability (Cont’d) Uncertainty Wednesday: Probability (Part 3) Uncertainty Wednesday: Two Week Hiatus Uncertainty Wednesday: PSA Test Example Uncertainty Wednesday: PSA Test Example (Cont’d) Uncertainty Wednesday: PSA Test Example (Part 3) Uncertainty Wednesday: PSA Test Example (Part 4) Uncertainty Wednesday: Sensitivity and Specificity Uncertainty Wednesday: Sensitivity and Specificity (Cont’d) Uncertainty Wednesday: Recap Uncertainty Wednesday: Independence Uncertainty Wednesday: Independence (Cont’d) Uncertainty Wednesday: Independence (Coin Flipping) Uncertainty Wednesday: Intro to Measuring Uncertainty Uncertainty Wednesday: Probability Distribution Uncertainty Wednesday: Entropy Uncertainty Wednesday: Entropy (Cont’d) Uncertainty Wednesday: Entropy and Communication Uncertainty Wednesday: Random Variables Uncertainty Wednesday: Expected Value Uncertainty Wednesday: Variance Uncertainty Wednesday: Continuous Random Variables Uncertainty Wednesday: Continuous Random Variables (Cont’d) Uncertainty Wednesday: Functions of Random Variables Uncertainty Wednesday: Jensen’s Inequality Uncertainty Wednesday: Risk Aversion (Jensen’s Inequality Cont’d) Uncertainty Wednesday: Risk Seeking (Jensen’s Inequality Cont’d) Uncertainty Wednesday: Weather (Intro) Uncertainty Wednesday: Weather - Climate Uncertainty Wednesday: Sample Mean Uncertainty Wednesday: Sample Mean (Cont’d) Uncertainty Wednesday: Sample Mean (Part 3) Uncertainty Wednesday: A Random Variable without Expected Value Uncertainty Wednesday: Fat Tails Uncertainty Wednesday: Interlude (Random Variable vs Distribution) Uncertainty Wednesday: Sample Mean under Fat Tails Uncertainty Wednesday: Sample Mean under Fat Tails (Cont’d) Uncertainty Wednesday: Recap on Sample Behavior (Inference) Uncertainty Wednesday: Suppressed Volatility Uncertainty Wednesday: Suppressed Volatility (Cont’d) Uncertainty Wednesday: Sample Variance Uncertainty Wednesday: Sample Variance (Cont’d) Uncertainty Wednesday: Spurious Correlation (Intro) Uncertainty Wednesday: Spurious Correlation (Cont’d) Uncertainty Wednesday: Spurious Correlation (Part 3) Uncertainty Wednesday: Correlation the Bayesian Way Uncertainty Wednesday: Interlude Uncertainty Wednesday: The Problem with P-Values (Intro) Uncertainty Wednesday: The Problem with P-Values (Incentives) Uncertainty Wednesday: The Problem with P-Values (Generating Hypotheses) Uncertainty Wednesday: The Problem with P-Values (Learning) Uncertainty Wednesday: Beliefs Uncertainty Wednesday: Beliefs (Cont’d) Uncertainty Wednesday: Beliefs (Part 3) Uncertainty Wednesday: Updating (Intro)

### Data guidance

https://vita.had.co.nz/papers/tidy-data.pdf

tidyverse

http://varianceexplained.org/r/teach-tidyverse/

https://github.com/datacarpentry/R-ecology-lesson/issues/378

http://samfirke.com/2017/06/15/how-to-teach-yourself-r/

https://github.com/unolibraries/workshops/tree/master/data-manipulation-r

http://varianceexplained.org/r/teach_ggplot2_to_beginners/

how to share data

https://github.com/jtleek/datasharing

https://www.theatlantic.com/science/archive/2018/04/the-scientific-paper-is-obsolete/556676/

How to work on a project

https://www.scrum.org/resources/scrum-guide

r markdown with git