Welcome to STA 199!

# Welcome to STA 199!
### Prof. Maria Tackett

---

# Welcome to STA 199!

---

### How are you feeling right now?

1) 🙂

4) 🤓

]

2) 😆

5) 🙁

]

6) 😎

]
]

---

## What is data science?

*"Data science is a concept to unify statistics, data analysis, machine learning and their related methods in order to understand and analyze actual phenomena with data. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, information science, and computer science."*

---

## Examples of data science

- [Analyzing trends in the Billboard Hot 100 over the past half century](https://towardsdatascience.com/billboard-hot-100-analytics-using-data-to-understand-the-shift-in-popular-music-in-the-last-60-ac3919d39b49)

- [Creating interactive redistricting maps](https://projects.fivethirtyeight.com/redistricting-maps/north-carolina/)

- [Tracking their life via Fitbit](https://livefreeordichotomize.com/2017/12/27/a-year-as-told-by-fitbit/)

- [US Spread of COVID-19 maps and analytics](https://www.sharedgeo.org/COVID-19/)

---

## Some of what you will learn

- Data visualization and wrangling with `ggplot2` and `dplyr` from the `tidyverse`

- Web scraping

- Web based applications with `RShiny`

- Spatial data visualization

]

- Version control with `GitHub`

- Reproducible reports with `R Markdown`

- Regression and classification

- Statistical inference
]

---

### Instructor: Prof. Maria Tackett

**Email**: [maria.tackett@duke.edu](mailto:maria.tackett@duke.edu)

.pull-left[
<img src="img/01/capital-one-logo.jpg" width="75%" style="display: block; margin: auto;" />

]

---

## Meet your classmates!

(~ 7 minutes)

- Go to a breakout room

- Introduce yourself!
  - Name
  - Year
  - What is something you're looking forward to this semester (either in this class or in general)?

- I'll visit some of the breakout rooms.

- *The breakout rooms are not recorded.*

---

## Practice with Zoom

- Display your name (first and last name)

- Raise your hand

- Reactions

- Chat

- Poll

- Breakout rooms (not recorded)

---

## Where to find information

.vocab[Course Website]: [sta199-fa20-002.netlify.app/](https://sta199-fa20-002.netlify.app/)
  - Central hub for the course!

.vocab[Sakai]
  - Gradebook
  - Class videos
  - Link to class meetings on Zoom
  
  
.vocab[GitHub]: [https://github.com/sta199-fa20-002](https://github.com/sta199-fa20-002)
  - Assignment repos (we'll talk more about that later)

---

## Textbooks and readings

- [OpenIntro Statistics](https://www.openintro.org/stat/textbook.php?stat_book=os)

- [R for Data Science](http://r4ds.had.co.nz/)

- Occasional articles and other readings posted on the course website

---

## Activities and assessments

- .vocab[Homework]: Individual assignments combining conceptual and computational skills.

- .vocab[Labs]: Individual or team assignments focusing on computational skills.

- .vocab[Exams]: Two take-home exams.

- .vocab[Final Project]: Team project presented during the final exam period.

- .vocab[Application Exercises]: Exercises worked on during the live lecture session.

- .vocab[Statistics Experiences]: Engage with statistics outside of the classroom and reflect on your experience.

---

## Where to find help in the course

- Attend .vocab[Office hours] to meet with a member of the teaching team.

- Use .vocab[Piazza] for general questions about course content and/or assignments, since other students may benefit from the response.

- Use email for questions regarding personal matters and/or grades.

---

## Lectures

- Focus on concepts behind data analysis

- Has two components:
  - **Lecture content videos** to watch before we meet
  - **Live lecture session** to ask questions and apply concepts from videos by working on exercises in small groups. 
  
- Ask questions by "raising your hand" or posting them in the chat.

- I encourage you to use your video if you are comfortable. 
---

## Course community

- Uphold the Duke Community Standard: 
> - I will not lie, cheat, or steal in my academic endeavors;
>- I will conduct myself honorably in all my endeavors; and
> - I will act if the Standard is compromised.

- Respect, honor, and celebrate our diverse community

- Learning environment that is welcoming and accessible to everyone

---

## Your Turn!

---

## Create a GitHub account

After you create your account, go to [bit.ly/sta199-github](https://bit.ly/sta199-github) and enter your Duke email address (NETID@duke.edu) and GitHub username. 
]

Some tips from [Happy Git with R](http://happygitwithr.com/github-acct.html#username-advice).

- Incorporate your actual name!
- Reuse your username from other contexts if you can, e. g., Twitter or Slack.
- Pick a username you will be comfortable revealing to your future boss.
- Be as unique as possible in as few characters as possible. Shorter is better than longer.
- Make it timeless.
- Avoid words with special meaning in programming (e.g. `NA`).

---

## Let's do an analysis!

- Let's take a look at the [UN Votes](https://sta199-fa20-002.netlify.app/appex/appex01-unvotes.html) analysis.

- Talk about the questions under the "Discussion" section in your breakout rooms.

- We'll choose a few rooms to share their conclusions.

---

[sta199-fa20-002.netlify.app](https://sta199-fa20-002.netlify.app)

Look under "Week 01" for this week's assignments.