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
大数据统计与计量分析方法 Big Data Statistics and Econometric Methods
2.
授课院系
Originating Department
信息系统与管理工程系 Division of Information Systems & Management Engineering
3.
课程编号
Course Code
MIS 210
4.
课程学分 Credit Value
3
5.
课程类别
Course Type
专业选修课 Major Elective Courses
6.
授课学期
Semester
春季 Spring
7.
授课语言
Teaching Language
英文 English
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）
Moris Strub
Department of Information Systems and Management Engineering
College of Business
Taizhou Building Rm 501-5(D)
strub@sustech.edu.cn
9.
实验员/助教、所属学系、联系
方式
Tutor/TA(s), Contact
待公布 To be announced
10.
选课人数限额(可不填)
Maximum Enrolment
（Optional）
11.
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
学时数
32
32
64
2
Credit Hours
12.
先修课程、其它学习要求
Pre-requisites or Other
Academic Requirements
MA212 概率论与数理统计 Probability and Statistics
13.
后续课程、其它学习规划
Courses for which this course
is a pre-requisite
14.
其它要求修读本课程的学系
Cross-listing Dept.
教学大纲及教学日历 SYLLABUS
15.
教学目标 Course Objectives
This is a major elective course where students will become familiar with important statistical tools and econometric
methods required to analyse big data in a modern business environment. This course aims to help students to gain the
skills to operate as a data scientist at a sophisticated data-driven firm. We will introduce the basic concepts, theories,
and methods of Econometrics and Big Data Statistics such as statistical analysis, econometric analysis, data modeling,
model selection, and optimal decision making. Besides learning about the underlying theoretical models and tools, a lot
of emphasis will be placed on real world applications and economic interpretations. Students will learn how to apply R to
perform big data analysis on real-world economic data.
16.
预达学习成果 Learning Outcomes
After finishing this course, students should be able to
a) know about the methodologies of econometrics and the foundations and techniques of big data statistics;
b) be familiar with common business problems and able to perform data analytics to drive better business decision-
making;
c) be familiar with the mathematical and economic theories, models, methods and algorithms for solving big data
problems relevant for the modern business world;
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.)
Lecture (32 hours)
Lec1 – Economic Data, Big Data, and the Nature of Econometrics [BDS0,ISL1,IE1]
In this lecture, students will be introduced Big Data and its characteristics, and discuss the scope of econometrics and
raises general issues that arise in the application of econometric methods.
Lec2 – Uncertainty and Probability [BDS1,IE-AppendixB&C]
In this lecture, students will learn the concept of uncertainty and review basic results from probability theory and
3
statistics
Lec3 – Linear Regression: Estimation I [BDS2,ISL3,IE2&3]
In this lecture, students will learn what is linear model, how to interpret the simple regression model and know about that
the simple regression model has limitations as a general tool for empirical analysis.
Lec4 – Linear Regression: Estimation II [BDS2,ISL3,IE2&3]
In this lecture, students will learn the multiple regression models and further discuss the advantages of multiple
regressions over simple regressions. Students will also know about how to estimate the parameters in the multiple
regression models using the method of ordinary least squares and describe various statistical properties of the OLS
estimators.
Lec5 – Linear Regression: Inference [ISL3,IE4]
In this lecture, students will continues learning multiple regression analysis, and turn to the problem of testing
hypotheses about the parameters in the population regression model, which includes that testing about individual
parameters, how to test a single hypothesis involving more than one parameter, and test multiple restrictions.
Lec6 – Linear Regression: Big Data Asymptotics [IE5]
In this lecture, students will learn the asymptotic properties or large sample properties of estimators and test statistics,
and know that even without the normality assumption, t and F statistics have approximately t and F distributions, at least
in large sample sizes.
Lec7 – Linear Regression: Qualitative Information [IE7]
In this lecture, students will learn to discuss qualitative independent variables, and know about how qualitative
explanatory variables can be easily incorporated into multiple regression models. Students will also learn to discuss a
binary dependent variable.
Lec8 – Linear Regression: Heteroskedasticity [IE8]
In this lecture, students will review the consequences of heteroskedasticity for ordinary least squares estimation, and
learn the available remedies when heteroskedasticity occurs, and also know about how to test for its presence.
Lec9 – Resampling Methods [BDS1,ISL5]
In this lecture, students will know the resampling methods: cross-validation and bootstraps, and be introduced K-fold
cross-validation, nonparametric and parametric bootstraps.
Lec11 – Model Selection [BDS3,ISL6]
In this lecture, students will know why we need fitting procedures other than least squares. Best subset selection,
forward selection and backward selection will be introduced. Students will also learn two common approaches that used
to select the best model, including indirect estimation of test error, Cp, AIC, BIC, adjusted R^2, and indirect estimation
of test error, validation and cross-validation to select the best model.
Lec12 – Regularization [BDS3,ISL6]
In this lecture, students will know why we need regularization to avoid overfitting in analysis. Students will learn
regularization paths. Ridge regression and lasso regression paths will be introduced. How to use cross-validation to
select the best model will be reviewed.
Lec13 – Classification I [BDS4,ISL4]
In this lecture, students will study the approaches for predicting qualitative responses - classification. And I will explain
why linear regression is not suitable in the case of a qualitative response. Students will be introduced K nearest
neighbours, discuss the relationship between probabilities and classification, and review logistic regression.
Lec14 – Classification II [BDS4,ISL4]
In this lecture, students will study multinomial logistic regression, distributed multinomial regression and MapReduce
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framework algorithm.
Lec15 – Clustering [BDS7,ISL10]
In this lecture, students will know the basic concepts of clustering and differences between clustering and classification.
And students will study the concepts of K-means clustering and hierarchical clustering and their algorithms. Besides,
practical issues in clustering will be introduced.
Lec16 – Factorization [BDS7,ISL10]
In this lecture, students will learn the concepts of principle components, and the procedure of principle component
analysis. Also, student will be introduced principle components regression (PCR) and its algorithm, and why PCR is
fruitful.
Lab (32 hours)
Lab1- Introduction to “R”- I [ISL2]
In this lab, “R”, its advantages and resources will be introduced. Students will know how to install and use R. And the
basic commands and graphics functions will be learned.
Lab2- Introduction to “R”- II [ISL2]
In this lab, students will know how to examine part of a set of data and how to load data.
Lab3- Linear Regression: Estimation I [ISL3]
In this lab, students will be Introduced library and know how to conduct simple linear regression with R
Lab4- Linear Regression: Estimation II [ISL3]
In this lab, students will know how to conduct multiple linear regression using lm( ) function with R and the usage of the
summary( ) function.
Lab5- Linear Regression: Inference [ISL3,IE4]
In this lab, students will learn how to perform statistical tests about parameters of a multiple linear regression model with
R.
Lab6- Linear Regression: Big Data Asymptotics [IE5]
In this lab, students will know how to conduct t-test and F-test when facing a large size of sample with R.
Lab7- Linear Regression: Qualitative Information [IE7]
In this lab, students will learn paths of qualitative predictor with R.
Lab8- Linear Regression: Heteroskedasticity [IE8]
In this lab, students will know how to perform test of heteroskedasticity for ordinary least squares estimation with R.
Lab9- Resampling Methods [BDS1,ISL5]
In this lab, students will know how to conduct Leave-one-out cross validation, k-fold cross-validation with R. Also,
students will learn how to perform bootstrap to estimate the accuracy of a statistic of interest and the accuracy of a
linear regression model with R.
Lab11- Model Selection [BDS3,ISL6]
In this lab, students will know how to conduct best subset selection with R and the associated function. Moreover,
students will learn to perform forward or backward stepwise selection using regsubsets( ) function with R.
Lab12- Regularization [BDS3,ISL6]
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In this lab, students will know how to perform ridge regression and lasso regression using glmnet( ) function with R.
Lab13- Classification I [BDS4,ISL4]
In this lab, students will learn to perform K-nearest neighbours using knn( ) function with R.
Lab14- Classification II [BDS4,ISL4]
In this lab, students will learn to conduct logistic regression glm( ) function with R.
Lab15- Clustering [BDS7,ISL10]
In this lab, students will learn how to conduct K-means clustering and hierarchical clustering with R.
Lab16- Factorization [BDS7,ISL10]
In this lab, principle component analysis will be introduced with R. NCI60 Data will be used to conduct PCA and
hierarchical clustering analysis.
18.
教材及其它参考资料 Textbook and Supplementary Readings
Matt Taddy, Business Data Science, McGraw Hill, 2019. [BDS]
Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani, An Introduction to Statistical Learning with
Applications in R, Springer, 2013. [ISL]
Jeffrey M. Wooldridge, Introductory Econometrics: A Modern Approach, 6th edition, 清华大学出版社（影印版, 2017. [IE]
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
10
课堂表现
Class
Performance
小测验
Quiz
课程项目 Projects
平时作业
Assignments
30
期中考试
Mid-Term Test
25
期末考试
Final Exam
35
期末报告
Final
Presentation
其它（可根据需要
改写以上评估方
式）
Others (The
above may be
modified as
necessary)
6
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
