class: title-slide .bg-text[ ## 程式設計與資料科學導論 ### week.1.orientation <hr /> 3月 4, 2021 謝舒凱 台大語言所 ] --- # 教練團 .pull-left[ 謝舒凱 <shukaihsieh@g.ntu.edu.tw>  廖永賦 古貿昌 王伯雅 石晴方 張鈺琳 ] -- .pull-right[  ] --- ## 這個世代的數位素養 ### 程式技能 + 資料科學 > The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades, not only at the professional level but even at the educational level for elementary school kids, for high school kids, for college kids. --- ## 程式教育反思 - 跟風可能安全，但是也容易迷失 - [某種勸退選方法](https://docs.google.com/presentation/d/1ZhUmH2eoIyjzRr6hHHjPy49HSVu6IlZf_ynK3WguYMs/edit#slide=id.gb7130772bc_0_26) --- # 所以這門通識課想要不太一樣 ### 素養重於技能（但沒有技能談不上有數位素養） -- - 數感（含統計知識） --  -- - 邏輯分析 -- - 敘事與圖說能力 -- - 人文社會關懷 --- # Data 無所不在的年代 > 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 --- # 數據的當代模樣 | Data in modern form (multimodality)  --- # Data applications  --- # 當代社會的數據之網 - <font color="red">以子之矛攻子之盾</font>  --- # 資料科學 | Data Science 操作層次 .pull-left[  ] -- .pull-right[  ] ??? slide 樣本參考 ： https://slides.yihui.name/xaringan/#27 https://yongfu.name/slides/xaringan/#28 --- # Data, Information and Knowledge (and maybe Wisdom)  --- # Data content analysis - 為何非結構性資料很重要  --- background-image: url(../img/emo/boredom-small.png) --- # 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. - 這門課的重點 --- # A Simple Example  --- 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 - 語言與文本分析、自然語言處理、機器學習 ??? 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 --- # 看遠一點，語言不是重點 - Python vs R (vs Julia vs ...) 找喜歡的 (授課老師 XD) 就好了 -- - **解決問題**才是王道！不然容易變成「我學過，但是 ...」這句口頭禪 --- # 怎麼學 參考助教的[學習歷程](https://yongfu.name/2018/01/31/RlearningPath.html) - 進階英文閱讀能力，培養高 GQ (google quotient) -- - <font color="blue">勸退方法 II</font>: 我的期待曾經是 3 hr/day -- - 難點在於如何持久的學習 - (~~3 hr/day~~ > 20-30 min/day，原子習慣！) --- # 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 --- # 定勢效應 ### 找出四個錯誤： `這句話友是四個錯唔` ??? --- # 教室在這裏 不好的炫技範例 ```r library(leaflet) leaflet() %>% addTiles() %>% setView(121.5373157,25.0182396, zoom = 17) ``` <div id="htmlwidget-41c10a731667369c60c7" style="width:504px;height:504px;" class="leaflet html-widget"></div> <script type="application/json" data-for="htmlwidget-41c10a731667369c60c7">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"calls":[{"method":"addTiles","args":["//{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",null,null,{"minZoom":0,"maxZoom":18,"tileSize":256,"subdomains":"abc","errorTileUrl":"","tms":false,"noWrap":false,"zoomOffset":0,"zoomReverse":false,"opacity":1,"zIndex":1,"detectRetina":false,"attribution":"© <a href=\"http://openstreetmap.org\">OpenStreetMap<\/a> contributors, <a href=\"http://creativecommons.org/licenses/by-sa/2.0/\">CC-BY-SA<\/a>"}]}],"setView":[[25.0182396,121.5373157],17,[]]},"evals":[],"jsHooks":[]}</script> ??? --> --- # 本門課程設計 - [課程網頁](https://lopentu.github.io/rlads2021/) - 評分標準與方式（平時作業 30%，期中考 30%，期末口頭與書面報告 40%） - 期中考、期末資料科學專題工作坊 （口頭或海報）[參考之前作品](https://sites.google.com/view/rlads-projects) - 課堂會用到的平台與工具：**Datacamp**, **Github (classroom)**, **sli.do**, (**NTU cool**) --- # 討論練習 with slido ## 如何課堂分組 - 定義分組的意思 operationalized definition - 為何要分組 (目的確認) motivation, exploratory data analysis - 如何分組與評估調整 algorithm, evaluation and adjustment 【using [Sli.do]()】 --- .pull-left[ ## 那就，開始學習吧！  ] .pull-right[  ]