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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml" lang="" xml:lang="">
<head>
<title>Introduction to Data Science with R</title>
<meta charset="utf-8" />
<meta name="author" content="謝舒凱" />
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<script src="libs/leaflet-binding/leaflet.js"></script>
</head>
<body>
<textarea id="source">
class: title-slide
.bg-text[
# Introduction to Data Science with R
### week.1.orientation
<hr />
9月 12, 2019
謝舒凱
]
---
# 教練團
.pull-left[
]
.pull-right[
- Shu-Kai Hsieh
- Yong-Fu Liao
- Yolanda Chen
- Jessy Chen
- Joy Chiang
]
---
# Data
> Information in raw or unorganized form (such as alphabets, numbers, or symbols) that refer to, or represent, conditions....they are generated from different sources like financial logs, text files, multimedia forms, sensors, and instruments.
???
科普 年薪
---
# Data in modern form (multimodality)
---
# Data applications
---
# 數據之網
---
# Data Science
操作層次
???
slide 樣本參考 ： https://slides.yihui.name/xaringan/#27
https://yongfu.name/slides/xaringan/#28
---
# Data, Information and Knowledge
---
# Data content analysis
- 為何非結構性資料很重要
---
# Text Mining and Text Analytics
- The science of extracting useful information from text, used for analyzing texts and turning them into a more structured form.
---
# Example
---
# Multimodality and Linguistics/Communication Science
---
class: top, right, inverse
background-image: url(https://miro.medium.com/max/830/1*1SiijZYTcRKKT_OnOFFlXw.jpeg)
background-color: black
# 資料科學相關學門 skill sets
???
Presenter's note presenter's note presenter's note presenter's note presenter's note
```
class: bottom, right, inverse
```
---
class: inverse, center, middle, title-slide
# R, why you
---
# 怎麼學
參考助教的[學習歷程](https://yongfu.name/2018/01/31/RlearningPath.html)
- 英文能力
- 3 hr/day
---
# 思維、習慣、態度
- [**Domain knowledge and social life**] 結合自己領域知識、拓展社會生活經驗
- [**Hacker Mindset**] 沒資料，找資料；有資料，動手做。
- [**Focused and Diffused mode**] 大腦的專注與發散模式
---
# 找出四個錯誤
`這句話友是四個錯唔`
???
定勢效應
---
background-image: url(../img/emo/boredom-small.png)
---
# 本堂課設計
- [課程網頁](https://rlads2019.github.io/)
- 評分標準與方式（作業 30%，期中 30%，期末 40%）
- 期中考、期末資料科學專題會議 （口頭或海報）
- 會用到的平台與工具：**Datacamp**, **Github (classroom)**, **sli.do**, **NTU cool**
---
# 錯誤示範：教室在這裏
```r
library(leaflet)
leaflet() %>% addTiles() %>% setView(121.5373157,25.0182396, zoom = 17)
```
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???
不好的炫技範例
-->
---
.pull-left[
## 那就，開始學習吧！
]
.pull-right[
]
</textarea>
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