【raining Objectives】

This major aims to cultivate senior professionals with good literacy in economics, mathematical statistics, and data analysis. Students are expected to proficiently apply statistical methods and software for data analysis, and possess basic capabilities for academic research. They should master the fundamental theories and methods of statistics, skillfully use computers for data identification, statistical inference, simulation analysis, multi-dimensional comprehensive evaluation of big data, etc., and be capable of engaging in data collection, big data modeling, econometric analysis, data mining (statistical learning), and other work in enterprises, public institutions, and management departments.

Taking positions such as statisticians, risk assessors, and big data engineers as target roles, graduates should be able to engage in statistical applied research and data analysis in government agencies, enterprises, public institutions, and economic and management departments.

【Economic Models and Econometric Analysis】

1.Economic models and econometric analysis constitute a core component of statistics. This disciplinary direction includes the application of modern cutting-edge statistical theories and methods to economic system modeling, econometric analysis, big data modeling and statistical inference, simulation, and calculation. Its main feature lies in emphasizing interdisciplinarity and integrating knowledge from fields such as economics, mathematical statistics, econometric analysis, computer science and technology, and mathematics. It employs methods like economic modeling and econometric analysis to conduct quantitative research on hot issues in economics and finance.

2.Mathematical Statistics

This disciplinary direction primarily focuses on researching several important issues in statistics, such as stochastic processes, empirical likelihood, statistical inference for high-dimensional data, statistical learning and pattern recognition, and statistical methods for big data. Its main feature is the improvement and optimization of dimensionality reduction methods for high-dimensional data, as well as more efficient analysis of big data.

3.Financial Statistics, Risk Management, and Actuarial Science

This disciplinary direction mainly studies the integration of modern statistical theories and methods in quantitative investment strategies, financial big data modeling, and risk management. Its key feature lies in comprehensively applying quantitative finance, statistical learning, artificial intelligence methods, and risk quantification models to research issues related to investment and risk quantification in financial markets.

4.Big Data Analysis

This disciplinary direction primarily focuses on interdisciplinary research into the generation and processing of big data, aiming to bridge the gap between data theories, data-driven applications, and services, enhance the level of big data applications, and realize the value of big data. Its main feature is the application of new biostatistical or data mining methods to analyze and study various types of big data in the field of medical health, in order to grasp their essence and regular patterns.

【Courses Offered】

Advanced Statistics, Multivariate Statistical Analysis, Research on National Economic Accounting, Advanced Macroeconomics and Microeconomics, Time Series Analysis, Advanced Quantitative Economics, Bayesian Statistics, Data Mining, Nonparametric Statistics, and Experimental Design.