2022级应用统计专业学位硕士研究生培养方案
所属学科门类:
所属一级学科:
所属院系:统计与信息学院
应用统计(025200)
本专业旨在培养德才兼备,具有家国情怀和国际视野,具备良好的政治素质与职业道德,系统掌握统计学的基本思想和收集、整理与分析数据的方法,并能根据应用领域数据的特点选用恰当的统计方法进行调查、分析、推断和预测,熟练应用统计软件并具备一定的编程能力,能够正确分析判断和解释统计软件的计算结果,毕业后能够在国家机关、经贸金融类企事业单位、咨询企业及社会组织,从事统计调查、数据分析和决策支持等工作的高层次、应用型统计专门人才。
二、学制
本专业学制为2年。在规定时期完成课程学习,但未完成学位论文者,可申请延长学习年限,累计最长年限不超过4年。
三、研究方向
1.数据科学与商务统计
2.大数据技术与贸易统计
3.金融科技与风险管理
4.人工智能与商务数据挖掘
四、课程设置与学分要求
本专业硕士研究生在攻读硕士学位期间应修满38学分,其中包括公共必修课7学分,学位必修课13学分,方向选修课至少7学分,专业选修课至少2学分,跨专业选修课2学分,实践教学7学分(含案例分析1学分,名师讲座2学分,社会实践4学分)。具体课程安排和学分见附表。
五、社会实践
根据本专业的培养方案,要求学生在研究生期间参加一定的社会实践,在政府及企事业单位的统计工作岗位实习实践不少于半年,参与和完成一项社会实际统计调查或数据分析的实践工作和实践报告。同时,须将思想政治教育融入社会实践,协同育人。
通过社会实践,培养学生的实践能力、分析问题和解决问题的能力以及综合运用所学基础知识和基本技能的能力,同时也增强学生适应社会的能力和就业竞争力。
具体要求见《上海对外经贸大学硕士研究生社会实践实施细则》。
六、培养方式
应用统计专业的课程均采取讲授、讨论和专题研究的方式进行。对应用统计专业研究生的培养实行双导师制,其中一位导师来自培养单位,另一位导师来自与本领域相关的校外专家。
七、学位论文
学位论文在导师指导下,由硕士研究生本人按计划进度独立完成。学位论文应与实际问题、实际数据和实际案例紧密结合,可采用与数据收集、整理、分析相关的数据分析报告、应用统计方法的实证研究等形式。
应用统计专业研究生的学位论文开题报告应在第2学期完成。学位论文的写作要求参照《上海市应用统计硕士专业学位论文基本要求和评价指标体系》和《上海对外经贸大学硕士学位论文内容和格式要求(2020年修订)》。
修满培养方案规定的学分、完成专业实习并通过学位论文答辩者,经学位评定委员会审核,授予应用统计硕士专业学位。
附表:
类别 | 课程名称 | 第1学期 | 第2学期 | 第3学期 | 学时 | 学分 | 开课部门 |
公共 课 | 中国特色社会主义理论与实践研究 | 2 | 36 | 2 | 马克思主义学院 | ||
马克思主义与社会科学方法论研究 | 1 | 18 | 1 | 马克思主义学院 | |||
高级英语口语与写作 | 2 | 36 | 2 | 国际商务外语学院 | |||
统计软件(英) | 2 | 36 | 2 | 统计与信息学院 | |||
学 位 必 修 课 | 学术规范与论文写作 | 1 | 18 | 1 | 统计与信息学院 | ||
高等统计学 | 3 | 54 | 3 | 统计与信息学院 | |||
高级程序设计 | 2 | 36 | 2 | 统计与信息学院 | |||
数据分析与统计建模 | 2 | 36 | 2 | 统计与信息学院 | |||
高级数据库技术 | 2 | 36 | 2 | 统计与信息学院 | |||
机器学习 | 3 | 54 | 3 | 统计与信息学院 | |||
方 向 选 修 课 | 数据科学与商务统计方向 | ||||||
统计计算* | 2 | 36 | 2 | 统计与信息学院 | |||
复杂数据统计分析* | 2 | 36 | 2 | 统计与信息学院 | |||
算法设计与实践* | 2 | 36 | 2 | 统计与信息学院 | |||
商务大数据案例分析* | 2 | 36 | 2 | 统计与信息学院 | |||
优化方法与数据分析实践* | 2 | 36 | 2 | 统计与信息学院 | |||
大数据技术与贸易统计方向 | |||||||
高级计量经济学* | 3 | 54 | 3 | 统计与信息学院 | |||
国民经济核算理论与方法 | 3 | 54 | 3 | 统计与信息学院 | |||
国际贸易统计 | 3 | 54 | 3 | 统计与信息学院 | |||
全球价值链统计 | 2 | 36 | 2 | 统计与信息学院 | |||
数据科学技术与应用* | 2 | 36 | 2 | 统计与信息学院 | |||
金融科技与风险管理方向 | |||||||
金融计量学* | 2 | 36 | 2 | 统计与信息学院 | |||
金融工程* | 2 | 36 | 2 | 统计与信息学院 | |||
数理金融* | 3 | 54 | 3 | 统计与信息学院 | |||
金融科技专题选讲* | 2 | 36 | 2 | 统计与信息学院 | |||
算法设计与实践* | 2 | 36 | 2 | 统计与信息学院 | |||
人工智能与商务数据挖掘方向 | |||||||
算法导论* | 2 | 36 | 2 | 统计与信息学院 | |||
文本挖掘技术* | 2 | 36 | 2 | 统计与信息学院 | |||
分布式计算* | 2 | 36 | 2 | 统计与信息学院 | |||
计算机视觉* | 2 | 36 | 2 | 统计与信息学院 | |||
深度学习* | 3 | 54 | 3 | 统计与信息学院 | |||
专业选修课 | 统计学前沿文献导读 | 1 | 18 | 1 | 统计与信息学院 | ||
贝叶斯统计 | 2 | 36 | 2 | 统计与信息学院 | |||
非参数统计 | 2 | 36 | 2 | 统计与信息学院 | |||
多元统计分析 | 2 | 36 | 2 | 统计与信息学院 | |||
广义线性及混合效应模型(英) | 2 | 36 | 2 | 统计与信息学院 | |||
试验设计与建模 |
| 2 | 36 | 2 | 统计与信息学院 | ||
国际贸易统计调查 | 2 | 36 | 2 | 统计与信息学院 | |||
经济数据挖掘与量化研究 | 36 | 2 | 统计与信息学院 | ||||
高频数据与量化交易 | 2 | 36 | 2 | 统计与信息学院 | |||
时空统计理论及应用 | 2 | 36 | 2 | 统计与信息学院 | |||
高级计量经济学(II) | 3 | 54 | 3 | 统计与信息学院 | |||
随机过程 | 2 | 36 | 2 | 统计与信息学院 | |||
数据挖掘 | 2 | 36 | 2 | 统计与信息学院 | |||
管理决策理论与方法 | 3 | 54 | 3 | 统计与信息学院 | |||
博弈论 | 2 | 36 | 2 | 统计与信息学院 | |||
Android移动应用开发 | 2 | 36 | 2 | 统计与信息学院 | |||
互联网前沿技术创新应用案例 | 2 | 36 | 2 | 统计与信息学院 | |||
强化学习基础 | 2 | 36 | 2 | 统计与信息学院 | |||
复杂系统与复杂网络 | 2 | 36 | 2 | 统计与信息学院 | |||
跨专业选修课 | 服务贸易与全球价值链 | 2 | 36 | 2 | 贸易谈判学院 | ||
可拓学专题 | 2 | 36 | 2 | 统计与信息学院 | |||
市场营销专题 | 2 | 36 | 2 | 工商管理学院 | |||
心理与行为研究方法 | 2 | 36 | 2 | 工商管理学院 | |||
企业与公司法 | 2 | 36 | 2 | 法学院 | |||
财务管理研究 | 2 | 36 | 2 | 会计学院 | |||
公司金融研究 | 2 | 36 | 2 | 金融管理学院 | |||
创业管理 | 2 | 36 | 2 | 工商管理学院 | |||
国际贸易实务 | 2 | 36 | 2 | 国际经贸学院 | |||
金融风险管理 | 2 | 36 | 2 | 金融管理学院 | |||
实践教学环节 | 案例分析 | 1 | 18 | 1 | |||
名师讲座 | 8次 | 2 | |||||
社会实践 | 4 | ||||||
注:加*方向选修课亦可作为其他方向专业选修课。 |
Masterin Applied Statistics Program(2022)
Field:Statistics:
Discipline:
School:School of Statistics
AppliedStatistics(025200)
I.Objectives
Thismajor aims to cultivate both ability and political integrity, withfamily and country feelings and international vision, with goodpolitical quality and professional ethics, systematic mastery of thebasic ideas of statistics and methods of collecting, collating andanalyzing data, and can choose appropriate statistical methods forinvestigation, analysis, inference and prediction according to thecharacteristics of data in the field of application, skilledapplication of statistical software and have certain programmingability, can correctly analyze and judge and interpret thecalculation results of statistical software, and can be in stateorgans after graduation. High-level and application-orientedstatistical professionals engaged in statistical surveys, dataanalysis and decision support, economic, trade and financialenterprises, consulting enterprises and social organizations.
Ⅱ.Duration of the Program
Thefull-time student of this major is 2 years. Those who have completedthe course study within the prescribed period, but have not completedthe degree thesis, may apply to extend the study period. The maximumcumulative years of study is 4 years.
Ⅲ.Research Areas
1.Data Science and Business Statistics
2.Big Data Technology andTrade Statistics
3.Financial Technology andRisk Management
4.Artificial Intelligenceand Business Data Mining
Ⅳ.Courses and Credits
Duringthe master's degree, the students should complete 38 credits,including 7 credits for common required courses, 13 credits forrequired courses, at least 7 credits for direction elective courses,at least 2 credits for major elective courses, 2 credits forcross-specialty courses, 7 credits for practical teaching (including1 credit for case analysis, 2 credits for lectures, 4 credits forsocial practice). See the attached table for the specific coursearrangement and credits.
Ⅴ.Social Practice
Accordingto the training objectives of this major, students are required toparticipate in certain social practices during postgraduate period,and practice in the statistical work positions of the government,enterprises and institutions for no less than half a year,participate in and complete the practical work and report of a socialactual statistical survey or data analysis. At the same time, we mustintegrate ideological and political education into social practiceand educate people cooperatively.
Throughsocial practice, students' practical ability, problem-analyzing andproblem-solving skills, and the ability to comprehensively using thebasic knowledge and basic skills learned are also cultivated. At thesame time, students' ability to adapt to society and employmentcompetitiveness are also enhanced.
Forspecific requirements, please refer to the Detailed Rules for “theImplementation of social practice for master's degree students ofShanghai University of International Business and Economics”.
VI.Education Modes
Thecourses of applied statistics are all finished in the patterns oflecture, discussion and research in the selected topics. A dual tutorsystem is implemented for the cultivation of graduate studentsmajoring in applied statistics. One tutor comes from the trainingunit, and the other tutor comes from outside the school and expertsin the field.
VII.Degree Thesis
Underthe guidance of the tutor, the dissertation should be independentlycompleted by the master student himself according to the schedule.The dissertation should be closely combined with practical problems,actual data and actual cases. And it may take the form of dataanalysis reports related to data collection, collation and analysis,empirical research using statistical methods, etc.
Thethesis proposal for graduate students of applied statistics should becompleted in the second semester. The writing requirements of thethesis refer to 'Basic Requirements and Evaluation Index Systemof Thesis for Master’s Degree of Applied Statistics in Shanghai'and 'Requirements for Format of Thesis for Master’s Degree inShanghai University of International Business and Economics (Revisedin 2020)'.
Thosewho complete the credits specified in the training plan, finishprofessional internships and pass the thesis defense will be awardeda master's degree in applied statistics by the degree evaluationcommittee.
AttachedTable:
Category | CourseName | Semester | CreditHours | Credit | Department | ||
1 | 2 | 3 | |||||
Common RequiredCourses | Researchon Theory and Practice of Socialism with Chinese Characteristics(degree course) | 2 | 36 | 2 | Schoolof Marxism | ||
Researchon Marxism and Social Science Methodology | 1 | 18 | 1 | Schoolof Marxism | |||
AdvancedEnglish Speaking and Writing | 2 | 36 | 2 | Schoolof Foreign Language | |||
StatisticsSoftware (English) | 2 | 36 | 2 | Schoolof Statistics and Information | |||
RequiredCourses | AcademicStandards and Paper Writing | 1 | 18 | 1 | Schoolof Statistics and Information | ||
AdvancedStatistics | 3 | 54 | 3 | Schoolof Statistics and Information | |||
AdvancedProgramming | 2 | 36 | 2 | Schoolof Statistics and Information | |||
DataAnalysis and Statistical Modeling | 2 | 36 | 2 | Schoolof Statistics and Information | |||
AdvancedDatabase Technology | 2 | 36 | 2 | Schoolof Statistics and Information | |||
MachineLearning | 3 | 54 | 3 | Schoolof Statistics and Information | |||
DirectionElective Courses | DataScience and Business Statistics | ||||||
StatisticalComputing* | 2 | 36 | 2 | Schoolof Statistics and Information | |||
StatisticalAnalysis of Complex Data* | 2 | 36 | 2 | Schoolof Statistics and Information | |||
AlgorithmsDesign and Practice* | 2 | 36 | 2 | Schoolof Statistics and Information | |||
BusinessBig Data Case Analysis* | 2 | 36 | 2 | Schoolof Statistics and Information | |||
OptimizationMethod and Data Analysis Practice* | 2 | 36 | 2 | Schoolof Statistics and Information | |||
BigData Technology and Trade Statistics | |||||||
AdvancedEconometrics* | 3 | 54 | 3 | Schoolof Statistics and Information | |||
Theoryand Method of National Economic Accounting | 3 | 54 | 3 | Schoolof Statistics and Information | |||
InternationalTrade Statistics | 3 | 54 | 3 | Schoolof Statistics and Information | |||
GlobalValue Chain Statistics | 2 | 36 | 2 | Schoolof Statistics and Information | |||
DataScience Technology and Application* | 2 | 36 | 2 | Schoolof Statistics and Information | |||
FinancialTechnology and Risk Management | |||||||
FinancialEconometrics* | 2 | 36 | 2 | Schoolof Statistics and Information | |||
FinancialEngineering* | 2 | 36 | 2 | Schoolof Statistics and Information | |||
MathematicalFinance* | 3 | 54 | 3 | Schoolof Statistics and Information | |||
SelectedLectures on Financial Technology* | 2 | 36 | 2 | Schoolof Statistics and Information | |||
AlgorithmsDesign and Practice* | 2 | 36 | 2 | Schoolof Statistics and Information | |||
ArtificialIntelligence and Business Data Mining | |||||||
Introductionto Algorithms* | 2 | 36 | 2 | Schoolof Statistics and Information | |||
TextMining Technology* | 2 | 36 | 2 | Schoolof Statistics and Information | |||
DistributedComputing* | 2 | 36 | 2 | Schoolof Statistics and Information | |||
ComputerVision* | 2 | 36 | 2 | Schoolof Statistics and Information | |||
DeepLearning* | 3 | 54 | 3 | Schoolof Statistics and Information | |||
MajorElective Courses | Introductionto Frontier Literature of Statistics | 1 | 18 | 1 | Schoolof Statistics and Information | ||
BayesianStatistics | 2 | 36 | 2 | Schoolof Statistics and Information | |||
NonparametricStatistics | 2 | 36 | 2 | Schoolof Statistics and Information | |||
MultivariateStatistical Analysis | 2 | 36 | 2 | Schoolof Statistics and Information | |||
GeneralizedLinear Mixed Effects Model (English) | 2 | 36 | 2 | Schoolof Statistics and Information | |||
Designand Modeling of Experiments |
| 2 | 36 | 2 | Schoolof Statistics and Information | ||
InternationalTrade Statistics Research | 2 | 36 | 2 | Schoolof Statistics and Information | |||
DataMining and Quantitative Research in Economics | 2 | 36 | 2 | Schoolof Statistics and Information | |||
High-frequencyData and Quantitative Transaction | 2 | 36 | 2 | Schoolof Statistics and Information | |||
Theoryand Application of Spatio-temporal Statistics | 2 | 36 | 2 | Schoolof Statistics and Information | |||
AdvancedEconometrics(II) | 3 | 54 | 3 | Schoolof Statistics and Information | |||
StochasticProcess | 2 | 36 | 2 | Schoolof Statistics and Information | |||
DataMining | 2 | 36 | 2 | Schoolof Statistics and Information | |||
ManagementDecision Theory and Method | 3 | 54 | 3 | Schoolof Statistics and Information | |||
GameTheory | 2 | 36 | 2 | Schoolof Statistics and Information | |||
AndroidMobile Application Development | 2 | 36 | 2 | Schoolof Statistics and Information | |||
InternetLeading Technology Innovative Applications | 2 | 36 | 2 | Schoolof Statistics and Information | |||
ReinforcementLearning | 2 | 36 | 2 | Schoolof Statistics and Information | |||
ComplexSystems and Complex Networks | 2 | 36 | 2 | Schoolof Statistics and Information | |||
Cross-specialtyCourses | ServiceTrade and Global Value Chain | 2 | 36 | 2 | Schoolof Trade Negotiation | ||
SpecificLectures of Extenics | 2 | 36 | 2 | Schoolof Statistics and Information | |||
MonographicStudy on Marketing Management | 2 | 36 | 2 | Schoolof Management | |||
EmpiricalMethods in Psychology and Behavior Research | 2 | 36 | 2 | Schoolof Management | |||
MonographicStudy on Enterprise and Company Law | 2 | 36 | 2 | Schoolof Law | |||
Researchon Financial Management | 2 | 36 | 2 | Schoolof Accounting | |||
Researchin Corporate Finance | 2 | 36 | 2 | Schoolof Finance | |||
EntrepreneurialManagement | 2 | 36 | 2 | Schoolof Management | |||
Practiceof Import and Export | 2 | 36 | 2 | Schoolof Business | |||
FinancialRisk Management | 2 | 36 | 2 | Schoolof Finance | |||
PracticalTeaching | Casestudy | 1 | 18 | 1 | |||
Lectures | 8times | 2 | |||||
SocialPractice | 4 | ||||||
Note:* the above three elective courses can also be used asprofessional elective courses in other directions |