110年第1學期-1641 行銷資料科學實務與應用-使用Python機器學習為例 課程資訊

評分方式

評分項目 配分比例 說明
期中考 30
期末報告 30
作業與討論點名 40

選課分析

本課程名額為 40人,已有76 人選讀,尚餘名額-36人。


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授課教師

姜自強

教育目標

Regardless of company size, the adoption of data science and machine learning for marketing is witnessing an exponential rise in the industry. With this course, students will learn to implement data science techniques to identify the factors behind the successes and failures of marketing campaigns. Students also be able to understand and predict customer behavior, and create more effectively targeted and personalized marketing strategies. First get to grips with performing simple through to advanced tasks, such as extracting hidden insights from data and using them to make smart business decisions. The course will further guide students through understanding what drives sales and increases customer engagement for the products. students will also get up to speed with implementing machine learning to predict which customers are more likely to engage with your products and have a high lifetime value. In addition to this, students will focus on how to use machine learning techniques to understand different customer segments and recommend the right products for each customer. As students explore further chapters, students will not only learn how to gain insights into consumer behavior using exploratory analysis, but also discover the concept of A/B testing and implement it using Python. By the end of this course, students will be well-versed with a variety of data science and machine learning techniques to run and manage successful marketing campaigns for the business. 無論公司規模如何,資料科學和機器學習在市場營銷中的採用正見證著該行業的指數級增長。通過本課程,學生將學習實施數據科學技術,以確定營銷活動成功與失敗的因素。學生還能夠理解和預測客戶行為,並更有效地制定針對性和個性化的營銷策略。 首先要處理從簡單到高級的任務,例如從數據中提取隱藏的見解並使用它們來製定明智的業務決策。本課程將進一步指導學生理解推動銷售並增加產品客戶參與度的因素。學生還將快速實施機器學習,以預測哪些客戶更可能與您的產品互動並具有很高的終身價值。除此之外,學生將重點學習如何使用機器學習技術來理解不同的客戶群,並為每個客戶推薦合適的產品。隨著學生探索更多的章節,學生不僅將學習如何使用探索性分析來了解消費者行為,還將發現A / B測試的概念並使用Python來實現。 在本課程結束時,學生將精通各種數據科學和機器學習技術,以運行和管理業務成功的營銷活動。

課程資訊

參考書目

Hands-On Data Science for Marketing: Improve your marketing strategies with machine learning using Python and R

Class-note
Class Handout