1. 瞭解資料科學如何應用在行銷領域，協助解決行銷問題及優化行銷決策。
2. 熟悉R或Python程式基本語法，進行行銷數據分析。
3. 運用自動機器學習(Auto ML)軟體或平台進行資料探勘、AI建模的操作與分析。
4. 瞭解如何運用行銷大數據分析以建構行銷智能。
1. Understand how data science can be applied in the marketing field and help solve marketing problems and optimize marketing decisions.
2. Be familiar with the basic syntax of R or Python programs and conduct marketing data analysis.
3. Use automatic machine learning (Auto ML) software or platform to perform data exploration, AI modeling operations and analysis.
4. Learn how to use marketing large data analysis to build marketing intelligence.
1. 羅凱揚、蘇宇暉、鍾皓軒（2023）。行銷資料科學｜大數據x市場分析x人工智慧(第二版)，碁峰。
2. Hwang, Y. H. (2019). Hands-On Data Science for Marketing: Improve your marketing strategies with machine learning using Python and R.（中譯本：Yoon Hyup Hwang著，沈佩誼譯（2020）。行銷資料科學實務：使用Python與R，碁峰。）
3. 徐聖訓（2019）。一行指令學Python：用Pandas掌握商務大數據分析，全華圖書
（上課教材視需要將在上課時再行調整）
1. Luo Kaiyan, Su Yuxi, and Long Haoxiao (2023). Marketing Data Science | Large Data x Market Analysis x Artificial Intelligence (Second Edition), Greefeng.
2. Hwang, Y. H. (2019). Hands-On Data Science for Marketing: Improve your marketing strategies with machine learning using Python and R. (Chinese translation: by Yoon Hyup Hwang, translated by Shen Pei (2020). Marketing data science practice: using Python and R, Gree Feng. )
3. Xu Sheng Shu (2019). One-line instruction learning Python: Use Pandas to master large data analysis of business, full Chinese book
(The textbooks for the courses need to be adjusted during the courses)
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 課堂參與（課堂表現、出席率）課堂參與（課堂表現、出席率） Class Participation (Course Performance, Attendance Rate) |
20 | |
| 平時成績（包括作業報告、個案討論等）平時成績（包括作業報告、個案討論等） Regular achievements (including business reports, case discussions, etc.) |
30 | |
| 學期專題或數據分析實作（含期中報告、期末報告）學期專題或數據分析實作（含期中報告、期末報告） Study period topic or data analysis implementation (including mid-term reports and final reports) |
50 |
