Type of Credit: Elective
Credit(s)
Number of Students
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The mobile commerce coverage will help students learn about the systems and consumer behaviors that combine to make up the ecosystem, market player, and consumer behaviors to learn how mwebistes, mwallet, mpayment, mcoupon, and mvoucher operate through different sectors including retail, transport, and consumer banking. Students will recognize a range of emergent trends and relevant laws and guidelines, including privacy and data protection. This course will provide a balanced view of theory and practice, which should allow the student to use and build practical big data analytics and management systems. Because of big data, managers can know more about their businesses, and directly translate that knowledge into improved decision making and performance. As the tools and philosophies of big data spread, they will change long-standing ideas about the value of experience, the nature of expertise, and the practice of management. At the end of the course, students should be able to answer the following questions:
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能力項目說明
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The strategic content of the course will feature a broad review of significant management challenges before assessing value of mobile commerce and big data applications through case studies and empirical research articles. The tactical content will focus on a triad which gives a basic foundation in IT including digital commerce, IT startup challenges, and specific skills in managing big data projects. |
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
|
1 |
Mobile communications, monetizing mobile audiences. Weekly hours 4 |
3 |
1 |
|
2 |
3 |
1 |
|
|
3 |
3 |
1 |
|
|
4 |
Viewability of mobile performance, mobile security, data mining and machine learning algorithms.Weekly hours required: 4 |
3 |
1 |
|
5 |
3 |
1 |
|
|
6 |
3 |
1 |
|
|
7 |
Mobile payment and location-sensitive services. Weekly hours required: 4. |
3 |
1 |
|
8 |
3 |
1 |
|
|
9 |
3 |
1 |
|
|
10 |
Future m-commerce services and business models. Weekly hours required: 4.
|
3 |
1 |
|
11 |
3 |
1 |
|
|
12 |
3 |
1 |
|
|
13 |
Telematics, and pervasive computing. Weekly hours required: 4. |
3 |
1 |
|
14 |
3 |
1 |
|
|
15 |
3 |
1 |
|
|
16 |
Map Reduce and No SQL system. Weekly hours required: 4. |
3 |
1 |
|
17 |
3 |
1 |
|
|
18 |
3 |
1 |
|
Participation 50% Mid-term 20% Final 30% |
Nikhilesh Dholakia, Ruby Roy Dholakia, Mark Lehrer, Nir Kshetri, Global Heterogeneity in the Emerging M-Commerce Landscape, 2017.
Micheline Kamber, Data Mining: Concepts and Techniques, 2016
Tawfik Jelassi, Strategies for E-business: Creating Value through Electronic and Mobile Commerce, 2017
Geoffrey Elliot, Mobile Commerce and Wireless Computing Systems, 2017Peter Ghavami, Big Data Analytics Methods: Analytics Techniques in Data Mining, Deep Learning and Natural Language Processing, 2019
Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, Cathy O’Neil, 2019