---
title: "Introduction to Math 335"
author: J. Hathaway
params:
  day: 1
  ptitle: true
  pbackground: true
  dtype: "none"
---

# What is a data scientist? {data-background=#6897bb}

## A blend of programmer, statistician, and communicator that burns with curiosity.

- [Medium.com on data science](https://medium.com/towards-data-science/what-makes-a-good-data-scientist-engineer-a8b4d7948a86){target="blank"}
    - **Read through the bulleted items and rank the bolded phrases in order of importance.**
- **The truth:**  The definition is settling, but the professional title of data science still covers a broad set of people and skills.

## When we say "blend" we can just as easily be saying:

<span style="font-size:75px"><span style="color:darkorange">
An almost scientist that can't quite program, often isn't the best statistician in the room and asks too many questions.
</span></span>

## {data-background-iframe="http://www.byui.edu/mathematics/data-science"}

> * [BYU-I Data Science](http://www.byui.edu/mathematics/data-science)

# Learning & Grading {data-background=#6897bb}

## Why do we attend college?

## {data-background-iframe="https://www.youtube.com/embed/kO8x8eoU3L4?rel=0&amp;start=0"}

[Father Sarduci](https://www.youtube.com/embed/kO8x8eoU3L4?rel=0&amp;start=0){target="blank"}

## [A Message to Garcia](https://courses.csail.mit.edu/6.803/pdf/hubbard1899.pdf) {data-background=#6897bb}

<div align="center"; style="color:white;font-size:40px">The point I wish to make is this: McKinley gave Rowan a letter to be delivered to Garcia; Rowan took the letter and did not ask, 'Where is he at?' <br>

By the Eternal! there is a *person* whose form should be cast in deathless bronze and the statue placed in every college of the land. It is not book-learning *students* need, nor instruction about this and that, but a stiffening of the vertebrae which will cause them to be loyal to a trust, to act promptly, concentrate their energies: do the thing - 'Carry a message to Garcia!'</div>



## On Education

Anthropologists have reported that the hunter-gatherer groups they studied did not distinguish between work and play--essentially all of life was understood as play.

With the rise of schooling, people began to think of learning as children's work. The same power-assertive methods that had been used to make children work in fields and factories were quite naturally transferred to the classroom.

If children learn nothing else in school, they learn the difference between work and play and that learning is work, not play.

> - [Peter Gray](https://www.psychologytoday.com/us/blog/freedom-learn/200808/brief-history-education)

## [Jeffry R. Holland on learning](https://www.churchofjesuschrist.org/study/ensign/2018/12/making-your-life-a-soul-stirring-journey-of-personal-growth?lang=eng)

So let’s talk about learning. As a teacher at heart, I love the word and the idea, though I do think we should define it a little better than we usually do. ... I don’t just mean the accumulation of knowledge, though that is part of it. I also don’t just mean passively listening to a lecture or memorizing facts. **I mean learning in the sense of growth and change, of insight leading to improvement, of knowing the truth, which in turn leads us closer to the God of all truth.**

<span style="font-size:45px"><span style="color:darkorange">
- What is the difference between *'accumulation of knowledge'* and *'knowing the truth'*?
</span></span>

## Syllabus {data-background=#6897bb}
 
### [MATH/CS 335 Syllabus](https://byuistats.github.io/M335/syllabus.html){target="blank"}

> * Preparation: **What are the class expectations on preparation?**
> * Performance: **How will assignments be "graded"?**
> * Semester Grade:  **How will you know what grade you earn?**

## MATH/CS 335 Philosophy

> - In a specifications-grading system all tasks are evaluated on a high-standards pass/fail basis using detailed checklists of task requirements and expectations. 
> - Letter grades are earned by passing marks on a set of tasks. 
> - This system provides for a variety of choice and is closer to how learning, and work, is done in the real world.
> - It will be easy for us to tell if work is complete, done in good faith, and consistent with the requirements.

# Understanding R & R-Studio {data-background=#6897bb}

## The big picture of R & R-Studio

> - [Socrative.com](https://socrative.com/) **Room ID: BYUICLASS**

## Getting R-Studio under control

- Tweaking the settings
    - [Google R Style Guide](https://google.github.io/styleguide/Rguide.xml){target="blank"}
- [Building our first .`Rmd` file](https://byuistats.github.io/M335/rmarkdown_help.html){target="blank"}
    - Understanding projects
    - Understanding `yaml`
    - knitting vs console
    - keeping the `.md` file
    
## Coding in groups & R-lab

> -https://byuistats.github.io/M335/lab.html

# Is there an art to making visualizations? {data-background=#6897bb}

## How humans see data -- John Rauser

> Above all else, show the data

**Tufte**

## {data-background-iframe="https://docs.google.com/spreadsheets/d/13YcmNLdbrk3QiOLdDs7RP8t9bKpvz83bXGq-rt5yhCc/edit#gid=0"}

> - [Google Spreadsheet](https://docs.google.com/spreadsheets/d/13YcmNLdbrk3QiOLdDs7RP8t9bKpvz83bXGq-rt5yhCc/edit#gid=0){target="blank"}
> - [Rasuer's Presentation](https://www.youtube.com/embed/fSgEeI2Xpdc?rel=0&amp;start=0){target="blank"}

## {data-background-iframe="https://www.youtube.com/embed/fSgEeI2Xpdc?rel=0&amp;start=1740"}

[Rasuer's Presentation](https://www.youtube.com/embed/fSgEeI2Xpdc?rel=0&amp;start=0){target="blank"}

## Hathaway highlights from Rauser

> - [notes](https://byuistats.github.io/M335/presentations_class/rauser_notes.html){target="blank"}

# Weekly Process {data-background=#6897bb}

## Class Tasks

*These tasks can be challenging but should be less time consuming than the weekly case studies. The first two weeks are not as consistent as the remaining weeks.*

> - Take notes on your reading of the specified 'R for Data Science' chapter in the README.md or in a '.R' script in the class task folder

or

> - Take notes on your reading of the new R package in the README.md or in a '.R' script in the class task folder

## Case Study Format

- [Week 1](https://byuistats.github.io/M335/weekly_projects/cs01_details.html){target="blank"} and [Week 2](https://byuistats.github.io/M335/weekly_projects/cs02_details.html){target="blank"} are slightly different.
- [Week 3](https://byuistats.github.io/M335/weekly_projects/cs03_details.html){target="blank"} begins the standard case study format.
    1. Open ended question to answer with data provided    
    ...   
    2. Compile your `.md` and `.html` file into your git repository
    3. Find two other student's compiled files in their repository and provide feedback using the issues feature in GitHub (If they already have three issues find a different student to critique)
    4. Address 1-2 of the issues posted on your project and push the updates to GitHub

# Class workload {data-background=#6897bb}

## To get an A

```{r, echo=FALSE, warning=FALSE, message=FALSE, fig.width=14}
library(tidyverse)

weeks <- tibble(Week = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), 
       low  = c(4, 8, 10, 10, 12, 11, 9, 5, 6, 6, 6, 6, 6),
       high = c(6, 12, 14, 14, 16, 14, 12, 9, 9, 9, 9, 11, 11))


weeks %>%
  ggplot(aes(x = Week)) +
  geom_point(aes(y = low, color = "Minimum"), size = 2.5) +
  geom_line(aes(y = low, color = "Minimum")) +
  geom_point(aes(y = high, color = "Max"), size = 2.5) +
  geom_line(aes(y = high, color = "Max")) +
  labs(y = "Hours per week", x = "Week of semester", title = "Number of hours to earn an A") +
  theme_bw() + theme(panel.grid.minor.x = element_blank()) +
  coord_cartesian(ylim = c(0, 20)) + 
  scale_x_continuous(breaks = 1:13) + scale_color_manual("Range", values = c("dodgerblue3", "orange3"))

```

## To get a B

### You don't do **B** work on all the tasks and case studies - You do **A** work on half of them.

* Focus on the first 5-weeks material. Pick and choose work after the foundation material.
* I know everyone can earn a B or higher.
