| 课程号 |
00333712 |
学分 |
3 |
| 英文名称 |
Data Driven Techniques for e-Business |
| 先修课程 |
不需要先修其他课程 |
| 中文简介 |
This course introduces the fundamentals of data driven techniques for e-business. Students will learn from this course the essential techniques for e-business organizations to make use of the now available big data streams about their customers. Big data has emerged as critical source of competitive advantage for e-business, and as a system of knowledge that is already changing the objects of knowledge and promises to bring new insights to our understanding of human networks and communities. Data driven techniques provide the ability to gain insight from such large scale, and fast changing data streams derived from phenomena where the underlying objects of interest are related in a complex manner. In the digital world, our every browse, every click, every review we read and write, every purchase we make, and so much more are all stored in the databases of relevant organizations. Leveraging from this digital archive, organizations use data driven techniques to learn about us, and to provide us with ever smarter and better experience. This course aims at familiarizing students with the latest developments and innovations in this fast growing area of data driven techniques in e-business, and equipping them with relevant knowledge and skills for the corresponding real life applications. |
| 英文简介 |
本课程介绍电子商务中数据驱动技术的基础知识。学生将从本课程中学习电子商务组织利用现有客户大数据流的基本技术。大数据已经成为电子商务竞争优势的关键来源，作为一个知识体系，它已经在改变知识的对象，并有望为我们对人类网络和社区的理解带来新的见解。数据驱动技术提供了从如此大规模、快速变化的数据流中获得洞察力的能力，这些数据流来自于底层感兴趣的对象以复杂方式关联的现象。在数字世界中，我们的每一次浏览、每一次点击、每一次读写评论、每一次购买，等等，都存储在相关组织的数据库中。利用这个数字档案，组织使用数据驱动的技术来了解我们，并为我们提供更智能、更好的体验。本课程旨在让学生熟悉电子商务中数据驱动技术这一快速发展领域的最新发展和创新，并为他们提供相应的实际应用相关知识和技能。 |
| 开课院系 |
工学院 |
| 通选课领域 |
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| 是否属于艺术与美育 |
否 |
| 平台课性质 |
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| 平台课类型 |
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| 授课语言 |
英文 |
| 教材 |
无；
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| 参考书 |
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| 教学大纲 |
本课程介绍电子商务中数据驱动技术的基础知识。学生将从本课程中学习电子商务组织利用现有客户大数据流的基本技术。本课程旨在让学生熟悉电子商务中数据驱动技术这一快速发展领域的最新发展和创新，并为他们提供相应的实际应用相关知识和技能。
1. Analytics for Recommendation Systems
2. Customer Lifetime Value Modelling
3. Customer Retention-Churn Model
4. Fraud Detection
5. Natural Language Processing (NLP) Models for User Generated Content (UGC) analysis
6. Storytelling with Data: Visualization of Large and Complex Datasets
线上授课，将分为课堂讲授（65%）、文献阅读（15%）、讨论（20%）、报告（10%）等教学方式
Weekly Hands-on Exercises and Multiple-choice Quizzes – 75%
Final Group Project Presentation – 25%
Total 100%
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| 教学评估 |
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