2012年10月《哈佛商業評論》（Harvard Business Review）將資料科學家（data scientist）稱為「21世紀最性感的工作」（Data Scientist: The Sexiest Job of the 21st Century），這是因應大數據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。
此外，根據聯合新聞網UDN於2016-12-16的報導，在2016年12月15日於台南成功大學舉辦的全國大專校院研發主管會議中，教育部政務次長陳良基表示，大學現有課程必須翻轉、跨領域，教育部高教司長李彥儀也說，以議題為導向、結合跨領域專長的專題形式課程已是未來趨勢。議題導向的專題式課程，讓學生在自動自發尋求答案的過程中，自動自發學習需要的知識。在「設計思考」的概念中，目前程式設計的能力相當被看重，高教司調查目前全台大專院校學生中只有18.8%的學生有學習程式設計，教育部決定逐年增加，明年(2017年)增加到30%，後年40%、大後年50%。
因此，本課程旨在帶領同學進入大數據(Big Data)、R程式語言設計、問題解決與決策分析的理論與實務殿堂，提供學生一場域，以議題為導向、結合跨領域專長的專題形式，學習有關資料科學與循證公共決策結合的基礎概念、與相關技術等，也為同學將來成為資料科學家做準備。
In October 2012, the Harvard Business Review called data scientists "Data Scientist: The Sexiest Job of the 21st Century", the most representative of the new vocations created by large data trends, which 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.
In addition, according to the report by UDN on 2016-12-16, at the National College Research and Development Conference held at Tainan Chenggong University on December 15, 2016, Chen Liangji, Deputy Minister of Education of the Ministry of Education, said that existing courses at universities must be translated and cross-regional. Li Junyi, Director of Higher Education of the Ministry of Education, also said that the courses in the form of topic-oriented and combining cross-regional leaders are already a trend. A topic-oriented topic-oriented course allows students to automatically develop the required knowledge in the process of automatically developing the answers. In the concept of "design thinking", the ability of programming is currently valued. According to the Higher Education Department, only 18.8% of students in colleges and universities across Taiwan have learning programming. The Ministry of Education has decided to increase it year by year, and it will increase to 30% next year (2017), 40% next year and 50% next year.
Therefore, this course aims to lead students into the theories and practice halls of Big Data, R program language design, problem solving and decision analysis, and provide students with a field of topics that are directed by topics and combine cross-domain specialists, learn basic concepts and related technologies that combine data science and certification public decision-making, and prepare students to become data scientists.
1. 邁向大數據的第一步！R語言程式設計精要。Jared P. Lander著，鍾振蔚譯。台北市：旗標資訊，2017年12月。
2. 中、英文書籍與期刊論文。
1. The first step towards large data! R language programming essence. By Jared P. Lander, Long Zhenwei. Taipei City: Flag Mark Information, December 2017.
2. Chinese and English books and journals.
| 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 |
40 | |
| 個人專題一（口頭簡報12%；書面報告18%）個人專題一（口頭簡報12%；書面報告18%） Personal topic 1 (12% of verbal reports; 18% of verbal reports) |
30 | 以授課教師提供的資料分析；簡報與書面報告大綱—背景、欲解決的問題、資料內容與來源、資料處理與分析、分析結果、對政策的啟示。 |
| 個人專題二（口頭簡報12%；書面報告18%）個人專題二（口頭簡報12%；書面報告18%） Personal topic 2 (12% of verbal reports; 18% of verbal reports) |
30 | 自行蒐集資料與訂定主題，單一資料集應有超過100,000筆資料。簡報與書面報告大綱，如個人專題一。 |
