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 is applied in the marketing field to help solve marketing problems and optimize marketing decisions.
2. Be familiar with the basic syntax of R or Python programs to conduct marketing data analysis.
3. Use automatic machine learning (Auto ML) software or platforms to perform data exploration and AI modeling operations and analysis.
4. Understand how to use marketing big 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 Kaiyang, Su Yuhui, and Zhong Haoxuan (2023). Marketing Data Science | Big Data x Market Analysis x Artificial Intelligence (Second Edition), Qi Feng.
2. Hwang, Y. H. (2019). Hands-On Data Science for Marketing: Improve your marketing strategies with machine learning using Python and R. (Chinese translation: written by Yoon Hyup Hwang, translated by Shen Peiyi (2020). Marketing Data Science Practice: Use Python and R, Qi Feng)
3. Xu Shengxun (2019). Learn Python with one line of instructions: Master business big data analysis with Pandas, Quanhua Books
(Textbooks will be adjusted during class if necessary)
| 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 (class performance, attendance) |
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） Daily results (including homework 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） Semester special topic or data analysis implementation (including mid-term report and final report) |
50 |
