介紹資料探索的方法及各種機器學習和資料探勘理論，利用R語言及Python的使用進行實際數據分析。學生必須進行分組比賽，並於每週與教師和其他同學討論他們的發現以及遇到的問題。期能藉由解決實際問題的過程累積實戰經驗並厚實計算的能力。

Introduce the methods of data exploration and various machine learning and data exploration theory, and use the use of R language and Python for actual data analysis. Students must conduct sub-competitions and discuss their findings and problems with teachers and other classmates every week. In order to accumulate practical experience and make the calculation ability through the process of solving actual problems.

在這大數據時代，不管是智慧製造、智慧醫療還是智慧經濟都仰賴數據的分析結果以作正確的決策。因此，培養數據科學家是當務之急。在本課程中，我們介紹資料探索的方法及各種機器學習和資料探勘理論，並透過分析幾筆大數據比賽的資料來訓練學生解決問題的能力及累積分析資料的經驗。我們擬以互動的方式進行本課程，讓學生進行分組比賽，過程中每組與教師和其他同學討論他們的發現以及他們每週遇到的問題。

In this era of data, whether it is smart manufacturing, smart medicine or smart economy, we rely on data analysis results to make correct decisions. Therefore, it is urgent to train data scientists. In this course, we introduce the methods of data exploration and various machine learning and data exploration theory, and train students' ability to solve problems and accumulated data analysis by analyzing data from several large data competitions. We conduct this course interactively, allowing students to conduct sub-group competitions, each group discusses their findings and the problems they encounter each week with teachers and other classmates.

Bradley Efron, Trevor Hastie (2016). Computer Age Statistical Inference:Algorithms, Evidence and Data Science. Cambridge University Press.

Bradley Efron, Trevor Hastie (2016). Computer Age Statistical Inference:Algorithms, Evidence and Data Science. Cambridge University Press.