當愈來愈多有關於 Big Data（大數據）能夠在網路世代改變人們的生活的故事與報導呈現出來，但是究竟神奇的 Big Data 要如何運作？除了資工之外，數學、統計等其他專業學門在資料科學中分別扮演什麼角色？在股市、在民調、在醫療等形形色色的領域之中，是否已經是能夠被成熟運用的產品了呢？因此當你想了解以下問題時：
1. 資料科學如何解決真實世界的問題？
2. 站在 AI 浪頭上，訓練電腦成為決策代理人的核心概念。
3. 文字也是數據，語意分析掌握電腦背後的情感。
4. 從演算法到金融交易，數學在資料科學中無所不在。
5. 透過資訊工程和統計分析相輔相成，提昇大數據可信度。
6. 當工業機器人大軍來襲，產業如何轉型與升級？
我們建議你來選修這們課程，他從範例學系、案例分享、書報討論、以及和老師分組研究等，認識大數據或是所謂資料科學的未來與潛力。

As more and more stories and reports emerge about how Big Data can change people's lives in the Internet era, but how does the magical Big Data work? In addition to payroll, what roles do other professional disciplines such as mathematics and statistics play in data science? Is it a product that can be maturely used in various fields such as the stock market, polling, and medical care? So when you want to know the following questions:
1. How does data science solve real-world problems?
2. Standing on the wave of AI, the core concept of training computers to become decision-making agents.
3. Text is also data, and semantic analysis grasps the emotions behind computers.
4. From algorithms to financial transactions, mathematics is omnipresent in data science.
5. Enhance the credibility of big data through information engineering and statistical analysis that complement each other.
6. When the army of industrial robots comes, how will the industry transform and upgrade?
We recommend that you take this elective course. From the example department, case sharing, book and newspaper discussions, and group research with teachers, you will learn about the future and potential of big data or so-called data science.

參考書目：

1. Data Mining : Practical Machine Learning Tools and Techniques, Ian H. Witten, Eibe Frank, Mark Hall, Christopher Pal, 4th Ed., 2017.

2. ViktorMayer-Schonberger、Kenneth Cukier，《大數據：教育篇：教學與學習的未來趨勢 (Learning with Big Data: The Future of Education)》，天下文化，2014/09/29

3. Viktor Mayer-Schonberger、Kenneth Cukier，《大數據(Big Data:A Revolution That Will Transform How We Live, Work, and Think)》，天下文化 ，2013/05/30

4. 城田真琴，《Big Data大數據的獲利模式：圖解．案例．策略．實戰》，經濟新潮社 ，2013/08/10

5. 車品覺，《大數據的關鍵思考：行動×多螢×碎片化時代的商業智慧》，天下雜誌，2014/12/24

6. Nate Silver，《精準預測：如何從巨量雜訊中，看出重要的訊息？(The Signal and the Noise: Why So Many Predictions Fail—but Some Don’t)》，三采，2013/09/06

7. Joel Gurin，《開放資料大商機：當大數據全部免費！創新、創業、投資、行銷關鍵新趨勢 (Open Data Now)》，時報出版，2015/04/17

8. David Thompson、Michael Fertik，《數位口碑經濟時代：從大數據到大分析的時代，我們如何經營數位足跡，累積未來優勢(The Reputation Economy)》，三采文化，2015/04/30

9. Christopher Surdak，《大數據時代的致勝決策：2020年前最重要的6個關鍵策略 (Data Crush)》，商周出版，2015/05/07

Bibliography:

1. Data Mining: Practical Machine Learning Tools and Techniques, Ian H. Witten, Eibe Frank, Mark Hall, Christopher Pal, 4th Ed., 2017.

2. Viktor Mayer-Schonberger, Kenneth Cukier, "Learning with Big Data: The Future of Education", Tianxia Culture, 2014/09/29

3. Viktor Mayer-Schonberger, Kenneth Cukier, "Big Data: A Revolution That Will Transform How We Live, Work, and Think", Tianxia Culture, 2013/05/30

4. Shirota Makoto, "Big Data's Profit Model: Illustration." Case. Strategy. Practical Combat", Economic News Society, 2013/08/10

5. Che Pinjue, "Key Thoughts on Big Data: Action × Multiple Fireflies × Business Wisdom in the Fragmented Era", Tianxia Magazine, 2014/12/24

6. Nate Silver, "Accurate Forecasting: How to See Important Information from Huge Noise?" (The Signal and the Noise: Why So Many Predictions Fail—but Some Don’t)”, Sancai, 2013/09/06

7. Joel Gurin, "The big business opportunity of open data: When big data is all free!" Key New Trends in Innovation, Entrepreneurship, Investment, and Marketing (Open Data Now)", Times Publishing, 2015/04/17

8. David Thompson, Michael Fertik, "The Era of Digital Reputation Economy: From the Era of Big Data to Big Analysis, How Do We Manage Digital Footprints and Accumulate Future Advantages (The Reputation Economy)", Sancai Culture, 2015/04/30

9. Christopher Surdak, "Winning Decisions in the Big Data Era: The 6 Most Important Key Strategies Before 2020 (Data Crush)", Shangzhou Publishing, 2015/05/07