本課程主要是針對統計系及資工系開課，也歡迎管理學院修習過統計學課程的學生選修。透過課程講授、個人作業與分組期末報告，在學期末時，修課學生應該具備下面的觀念與技能： (1) 了解什麼資料用什麼方法。 (2) 瞭解資料分析與探勘的過程與步驟方法。 (3) 使用R或Python軟體進行程式撰寫與統計分析。 (4) 視覺化資料進行溝通。 (5) 針對一個有趣或產業的問題，運用教授的統計學習與R、Python語言，進行真實資料的分析。

This course is mainly for the Department of Statistics and Qualifications, and students who have passed the Statistics course are also welcome to choose from. Through course lectures, personal work and sub-substantiated final reports, at the end of the year, students in the course should have the following concepts and skills: (1) What methods should be used to understand what information is used. (2) Understand the process and step-by-step methods of data analysis and exploration. (3) Use R or Python software for programming and statistical analysis. (4) Visualize the data for communication. (5) Use the professor's statistical learning and R and Python languages ​​to analyze real data for an interesting or industrial problem.

1.Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, An Introduction to Statistical Learning with Applications in R.
2.Trevor Hastie, Robert Tibshirani and Jerome Friedman, The Elements of Statistical Learning, Data Mining, Inference, and Prediction. Second Edition.
3.R語言機器學習。吳金朝 譯。

1.Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, An Introduction to Statistical Learning with Applications in R.
2.Trevor Hastie, Robert Tibshirani and Jerome Friedman, The Elements of Statistical Learning, Data Mining, Inference, and Prediction. Second Edition.
3.R language machine learning. Wu Jinchao Translation.