1
课程详述
COURSE SPECIFICATION
以下课程信息可能根据实际授课需要或在课程检讨之后产生变动。如对课程有任何疑问，请联
系授课教师。
The course information as follows may be subject to change, either during the session because of unforeseen
circumstances, or following review of the course at the end of the session. Queries about the course should be
directed to the course instructor.
1.
课程名称 Course Title
大数据管理与应用前沿讲堂 Lectures on the Frontiers of Big Data Management and
Applications
2.
授课院系
Originating Department
商学院 School of Business
3.
课程编号
Course Code
EBA103
4.
课程学分 Credit Value
2
5.
课程类别
Course Type
专业选修课 Professional Elective Courses
6.
授课学期
Semester
春季 Spring
7.
授课语言
Teaching Language
中英双语 English & Chinese
8.
授课教师、所属学系、联系方
式（如属团队授课，请列明其
他授课教师）
Instructor(s), Affiliation&
Contact
（For team teaching, please list
all instructors）
黄伟，商学院，hw-feba@sustech.edu.cn
江俊毅，商学院，jiangjy@sustech.edu.cn
何翘楚，商学院，heqc@sustech.edu.cn
李少波，商学院，lisb3@sustech.edu.cn
郭悦，商学院，guoy@sustech.edu.cn
雷洋，商学院，leiy@sustech.edu.cn
李媛媛，商学院，liyy3@sustech.edu.cn
顾里一，商学院，guly@sustech.edu.cn
Moris Strub，商学院，strub@sustech.edu.cn
叶茂亮，商学院，maoliang.ye@xmu.edu.cn
9.
实验员/助教、所属学系、联系
方式
Tutor/TA(s), Contact
无 NA
10.
选课人数限额(可不填)
Maximum Enrolment
（Optional）
2
授课方式
Delivery Method
讲授
Lectures
习题/辅导/讨论
Tutorials
实验/实习
Lab/Practical
其它(请具体注明)
Other（Please specify）
总学时
Total
11.
学时数
Credit Hours
32
32
12.
先修课程、其它学习要求
Pre-requisites or Other
Academic Requirements
无 None
13.
后续课程、其它学习规划
Courses for which this course
is a pre-requisite
无 None
14.
其它要求修读本课程的学系
Cross-listing Dept.
无 None
教学大纲及教学日历 SYLLABUS
15.
教学目标 Course Objectives
本课程旨在向对大数据管理与应用专业感兴趣的学生介绍大数据管理最前沿的研究，分享业界最新的应用；培养数据管理
和商务分析人才。通过本课程，有助于学生了解大数据行业和研究的趋势，对自身未来的学科发展进行初步规划。
This course aims to introduce the cutting-edge research of big data management and share the latest applications of the
industry to students who are interested in big data technology and management. With the help of this course, our final
gaol is to cultivate data management and business analysis talents. Through this course, students can understand
industry and research trends and make preliminary plans for future development.
16.
预达学习成果 Learning Outcomes
通过对本课程的学习，学生将对大数据管理与应用相关前沿研究产生一个整体认知，对于行业内著名企业在大数据上的应
用有一定了解，启发学生对未来发展进行规划。
Through the study of this course, students will have an overall understanding of the cutting-edge research
related to big data, have a brief understanding of the applications of big data by famous enterprises in
the industry. And this course will inspire students to plan their future development.
17.
课程内容及教学日历 （如授课语言以英文为主，则课程内容介绍可以用英文；如团队教学或模块教学，教学日历须注明
主讲人）
Course Contents (in Parts/Chapters/Sections/Weeks. Please notify name of instructor for course section(s), if
this is a team teaching or module course.)
3
星期
Week
主题
Topic
主讲人
Speaker
简介
Brief Introduction
1
“顶天立地“——大数据研究方向
的确定（The Determination of the
Research Direction of Big Data）
黄伟
大数据是交叉学科研究领域（跨管理科学和工商管
理等学科），涉及的研究问题和方向多且广，不易
确定具体的研究方向。本课程讲介绍 “顶天立地”
的研究方向如何确定。“顶天”的大数据研究即探
讨新涌现的首席数据执行官（CDO）如何将组织中
高层决策从经验导向型改变为数据导向型，从而使
组织高管能在大数据时代带领组织更上一层楼。
“立地”的大数据研究即数据质量的研究，这也是
大数据研究的基石。
Big data is an interdisciplinary research field
(spanning management science, business
administration and other disciplines), involving a
wide range of research issues and directions, and it
is difficult to determine the specific research
direction. This course introduces how to determine
the research direction of "Dingtian Lidi". "Dingtian"
means that the big data research explores how the
newly emerged chief data executive (CDO)
changes the decision-making of middle and senior
level of the organization from experience-oriented
to data-oriented, so that the senior management of
the organization can lead the organization to a
higher level in the era of big data. "Lidi" means that
the research of data quality is the cornerstone of
big data research.
2
项目管理视角下的组织战略变革
（Organizational Strategical
Change from the Perspective of
Project Management）
江俊毅
基于系统性观点实现组织战略目标，关注战略变革
与整合、不确定性与模糊性中潜在收益、机会最大
化，提供结构化项目管理方法。
Achieve the organization's strategic goals based on
a systematic perspective, focus on strategic
change and integration, maximize potential benefits
and opportunities in uncertainty and ambiguity, and
provide structured project management methods.
3
数据驱动的物流科技与服务运营管
理（Data-driven Logistics
Technology and Service Operation
Management）
何翘楚
运筹学和运营管理方法在主要的物流科技公司（阿
里，京东，顺丰等）和平台经济公司（滴滴出行
等）有广泛的应用。这节课将系统介绍数据科学在
这类应用中的角色：如何将运筹优化算法，统计机
器学习和经济管理分析方法整合起来提高企业运营
效率。
Operations research and operation management
methods are widely used in logistics technology
companies (Ali, JD, SF express, etc.) and platform
economy companies (Didi Chuxing, etc.). This
speech will systematically introduce the role of data
science in such applications as how to integrate
operational optimization algorithms, statistical
machine learning, and economic management
analysis methods to improve the operational
efficiency of enterprises.
4
大数据背景下的行为科学
（Behavioral science in the context
of big data）
李少波
这一话题将从最近几年的诺贝尔经济学奖得主的研
究切入，探讨大数据背景下行为科学的发展趋势和
未来走向。
Starting from the research of Nobel Prize winners
4
in economics in recent years, this topic will focus
on the development and future trend of behavioral
science in the context of big data.
5
大数据背景下的酒店与旅游行业的
商业运营（Business operation of
hotel and tourism industry under the
Context of big data）
郭悦
基于大数据的数字挖掘和分析技术已经在传统行业
例如酒店旅游行业有着越来越成功的运用。这节课
主要帮助学生了解大数据在这行业中的商业价值和
商业模式的创新和成功案例。
Business performances of companies in traditional
industries, e.g., the Hospitality and Travel industry
has been substantially improved, relying on Big
Data business intelligence from different aspects,
including marketing strategy, business model, and
business operation. This course is designed to help
students understand the business value of big data
in this industry.
6
物联网大数据在机场中的应用
雷洋
如何研究、开发和充分利用物联网数据来引进及实
现新型数据分析，以提高行李到达流程（BAP）的
性能。
How to research, develop and make full use of IoT
data to introduce and implement new data analysis
to improve the performance of baggage arrival
process.
7
大数据背景下的精准广告（Targeted
Advertising in the Context of Big
Data）
李媛媛
这一话题将从广告学的基本理论入手，结合大数据
背景下广告行业的发展，针对日益发展的精准营销
探讨未来广告行业的趋势和未来走向。
This topic will start with the basic theory of
advertising, combine with the development of the
advertising industry under the background of big
data, and discuss the future trend of the advertising
industry in view of the increasingly developed
precision marketing.
8
不确定性下的优化（Optimization
under Uncertainty）
顾理一
在各种业务和工程场景中，我们希望做出“最佳决
策”，但是关于问题的信息不完整，而且新信息通
常需要付出很高的代价。本课程旨在介绍几个这样
的案例，以及在这种不确定性下有效地找到一个最
优的解决方案。
In various business and engineering scenarios, we
would like to make the "best decision". But the
information about the problem is usually
incomplete. New information typically comes at a
high cost. This speech aims to introduce several
such cases, as well as some algorithms that can
find a good solution efficiently under such
uncertainty.
9
Mean-variance portfolio selection
Moris
Simon
Strub
How should one distribute one's wealth among
available assets to best satisfy one's financial
needs and objectives? We look at one of the
classical models addressing this question. In this
model, the investor seeks to determine the portfolio
with minimal risk among all those portfolios which
on average exceed a target return.
一个人应该如何在可用的资产中分配自己的财富，
以最好地满足自己的财务需求和目标?我们来看看解
决这个问题的一个经典模型。在这个模型中，投资
者试图在所有平均超过目标收益的投资组合中确定
5
风险最小的投资组合。
10
因果推断（Causal Inference）
叶茂亮
因果推断（causal inference）着重于揭示变量之间
的因果关系及政策事件的真实效应，是统计与大数
据方法论的核心问题之一，并在经济管理、社会科
学及项目评估中有广泛应用，本讲座将介绍实验和
拟实验（quasi-experiments）的常用方法。
Causal inference focuses on revealing the
relationship between variables and the real effects
of policy events. It is one of the core issues of the
methodology of statistics and big data and has
been widely applied in economic management,
social science and project evaluation. This lecture
will introduce the commonly used methods of
experiments and quasi experiments.
11
制造行业的大数据应用
制造行业
IT 部门总监
介绍大数据技术在制造行业中的应用
Introduce the applications in manufacturing
industry
12
大数据背景下的消费者行为分析
知名高校教
授
介绍如何利用大数据技术进行消费者行为分析
Introduce how to use big data technology to do
consumer behaviour analysis
13
保险行业中的大数据分析
保险公司
IT 部门负责
人
介绍大数据技术在保险行业中的应用
Introduce the applications of big data in insurance
company
14
互联网信息的数据分析
互联网公司
技术总监
介绍如何对互联网信息进行数据分析
Introduce how to analyse data from internet
15
金融行业中数据分析的应用
金融公司
CTO
介绍数据分析在金融行业中的应用
Introduce the applications of data analysis in
finance
16
利用大数据技术进行用户画像
知名企业
CTO
介绍如何利用大数据对用户进行画像
Introduce how to portrait the user based on big
data.
18.
教材及其它参考资料 Textbook and Supplementary Readings
无 None
课程评估 ASSESSMENT
19.
评估形式
Type of
Assessment
评估时间
Time
占考试总成绩百分比
% of final
score
违纪处罚
Penalty
备注
Notes
出勤 Attendance
根据学生的出勤，将
按照以下标准在学生最
终成绩中增加额外的分
数：全勤=15 分；缺勤
1 天=12 分；缺勤 2 天
(仅 2 天)=7 分
According to your
attendance the
following extra credit
不要忘记在出勤
表中签字。一旦
出勤表中你的签
字处为空白，你
当天将被视为缺
勤。
Do NOT forget
to sign the
attendance log.
只是在出勤表中签名而后离开的学生
将被标记为缺勤。
因为出勤算作额外加分，所以不接受
任何理由的缺勤！ 当然，特殊情况下
通过书面请假可以酌情考虑。
Those that just sign the attendance
log and then leave class shall be
marked absent.
Since attendance is extra credit, I do
NOT accept ANY excuses!
6
will be added to
YOUR final grade:
PERFECT
attendance, no days
missed = 15 points;
ONE (and only one)
absence = 12 points;
TWO (and only two) =
7 points
Once your
unsigned field
receives the
“blank mark”
you are officially
counted absent
for that day!
Documented evidence for exceptional
cases such as illness will be
considered.
课堂表现
Class
Performance
15%，根据学生的课
堂表现以及小组活动表
现进行给分。
There will be
opportunities to earn
credit during the
course through in-
class assignments
and group activities.
小测验
Quiz
课程项目 Projects
平时作业
Assignments
期中考试
Mid-Term Test
期末考试
Final Exam
期末报告
Final
Presentation
70%（总分 100）
70% （100 points in
total）
其它（可根据需要
改写以上评估方
式）
Others (The
above may be
modified as
necessary)
20.
记分方式 GRADING SYSTEM
A. 十三级等级制 Letter Grading
B. 二级记分制（通过/不通过） Pass/Fail Grading
课程审批 REVIEW AND APPROVAL
21.
本课程设置已经过以下责任人/委员会审议通过
This Course has been approved by the following person or committee of authority
