2012年10月《哈佛商業評論》（Harvard Business Review）將資料科學家（data scientist）稱為「21世紀最具吸引力的工作」（Data Scientist: The Sexiest Job of the 21st Century），這是因應大數據(Big Data)、或巨量資料潮流所造就的新職種中，最具代表性的，能透過電腦演算分析資料、解讀意義。資料科學家是未來職場中最炙手可熱的明星職業，根據資料軟體相關業者指出，具備資料蒐集與分析的碩士畢業生，「起薪起碼44Ｋ起跳！」如果有一年至兩年經驗的資料探勘人才，平均月薪甚至領到七萬元，都不是問題，換句話說，當上資料科學家，等於擁有一張年薪百萬元的入場券。
《哈佛商業評論》給資料科學家下了一個定義：「資料科學家是懂得從今日如海嘯般非結構化資訊中，撈出重要商業問題解答的一群人。」事實上，資料科學家不只是要像哥倫布般，在茫茫大海中打開探照燈，找出有用的資料，還要如偵探小說家愛倫坡一樣，審視手上的資料，推理出問題的答案。
本課程旨在帶領大學部學生進入資料科學與公共問題解決與決策分析的基本理論與實務殿堂，提供學生一場域，以公共議題為導向、結合跨領域專長的專題實作形式，學習有關資料科學與循證(evidence-based)公共決策結合的基礎概念、與初階之資料分析技術等，也為同學將來成為資料科學家做準備。
The Harvard Business Review in October 2012 called data scientists "Data Scientist: The Sexiest Job of the 21st Century", the most representative of the new jobs created by large data, or the huge data trend, can analyze data and interpret ideas through computer calculations. Data scientists are the most popular celebrity careers in the future. According to data software-related professionals, a graduating student with data collection and analysis, "starting salary starts at 44K!" If a data exploration talent with one year to two years experience has an average monthly salary of even 70,000 yuan, it is not a problem. In other words, being a data scientist is equivalent to having an entry voucher with an annual salary of one million yuan.
"Harvard Business Review" gives data scientists a definition: "Data scientists are a group of people who know how to answer important business questions from today's non-structured information like a sea." In fact, data scientists should not only open search lights in the vast ocean like Colembo and find useful information, but also examine the data in their hands and infer the answers to questions like the detective novelist Love Lenpo.
This course aims to lead university departments into the basic theories and practice halls of data science and public problem solving and decision analysis, providing students with a field of topics, using public issues as a guided and combined with cross-domain specialist practices, learning basic concepts that combine data science and evidence-based public decisions, and preparing students to become data scientists.
1. Cady, Field (2017). The Data Science Handbook, Hoboken, NJ: John Wiley & Sons, Inc.
2. EMC Education Services (2015). Data Science & Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Indianapolis, IN: John Wiley & Sons, Inc.
3. Magallanes Reyes, J. (2017). Introduction to Data Science for Social and Policy Research: Collecting and Organizing Data with R and Python. Cambridge: Cambridge University Press.
4. Baumer, Benjamin S., , Daniel T. Kaplan, , Nicholas J. Horton (2017). Modern Data Science With R, Boca Raton, FL: CRC Press.( https://beanumber.github.io/mdsr2e/)
5. 許弘毅、劉俐良、陳敦源，2020，邁向循證基礎的公共政策宣導：一個以臺灣酒駕防制廣告類型有效性為核心的準實驗研究，政治科學論叢，第84期，71-112。
6. 其他中、英文書籍與期刊論文、補充講義。
1. Cady, Field (2017). The Data Science Handbook, Hoboken, NJ: John Wiley & Sons, Inc.
2. EMC Education Services (2015). Data Science & Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Indianapolis, IN: John Wiley & Sons, Inc.
3. Magallanes Reyes, J. (2017). Introduction to Data Science for Social and Policy Research: Collecting and Organizing Data with R and Python. Cambridge: Cambridge University Press.
4. Baumer, Benjamin S., , Daniel T. Kaplan, , Nicholas J. Horton (2017). Modern Data Science With R, Boca Raton, FL: CRC Press.( https://beanumber.github.io/mdsr2e/)
5. Xu Hongyi, Liu Liliang, Chen Dunyuan, 2020, Mid-to-do-based public policy publicity: A practical experiment research centered on the effectiveness of Taiwan’s alcoholic prevention and control advertising types, Political Science Forum, Issue 84, 71-112.
6. Other Chinese and English books and journals and supplementary speeches.
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 作業與出席作業與出席 Work and attendance |
34 | |
| 個人專題一（口頭簡報16%；書面報告16%）個人專題一（口頭簡報16%；書面報告16%） Personal topic 1 (16% of verbal reports; 16% of verbal reports) |
32 | |
| 個人專題二（口頭簡報16%；書面報告16%）個人專題二（口頭簡報16%；書面報告16%） Personal topic 2 (16% of verbal reports; 16% of verbal reports) |
32 |
