Bachelor of Science
We are now offering two undergraduate programs, in Statistics and in Data Science and Big Data Technology.
Generally, first-year university students will be enrolled in the Maths-Statistics program and obtain credits for some basic courses. By the end of the first-year of college, students will be able to choose a specific program between the School of Mathematical Science and School of Statistics and Data Science based on their interest. Students enrolled by the School of Statistics and Data Science will have to choose between the program in Statistics and the Program in Data Science and Big Data Technology before the third-year of college.
The two undergraduate programs, in statistics and in Data Science and Big Data Technology, slightly differ from each other. For students in Statistics, they will be required to take more courses in statistical theories, while students in Data Science and Big Data Technology will have to take more courses in applications.
Our BSc Programs aim to equip students with a good foundation in mathematics, computer application skills, a mastery of the basic theories and methods of statistics, and a certain understanding and knowledge of economics, finance and insurance, with the ability to apply the theories they have learned to solve practical problems, as well as roficiency in English and the basic ability to listen, speak, read, write and translate.
Duration of Program:4 Years
Degree Conferred: Bachelor of Science in Statistics / Data Science and Big Data Technology.
Core Courses (updated in May 2022):
| No. | Course | Credit(s) |
| 1 | Mathematical Epidemiology: Modeling and Forecasting | 1 |
| 2 | Mathematical Analysis I | 5.5 |
| 3 | Mathematical Analysis II | 4.5 |
| 4 | Stochastic Calculus | 2 |
| 5 | Fundamentals of Information Theory | 2 |
| 6 | Survival Analysis | 3 |
| 7 | Bioinformatics | 2 |
| 8 | Fundamentals of algorithms | 3.5 |
| 9 | Mathematical Analysis 3-3 | 5 |
| 10 | Database System | 2.5 |
| 11 | Time Series Analysis | 3 |
| 12 | Distributed Storage and Computing in Statistics | 3.5 |
| 13 | Deep Learning | 3.5 |
| 14 | Natural language processing and text mining | 3.5 |
| 15 | Probability Theory II | 3 |
| 16 | Introduction to data science | 2 |
| 17 | exploratory data analysis | 2 |
| 18 | Data visualization | 2.5 |
| 19 | Software for Statistics and Big Data Analysis | 4 |
| 20 | Multivariate Statistical Analysis | 4 |
| 21 | Selected Topics in Modern Statistics | 2 |
| 22 | Graduation Thesis | 6 |
| 23 | Innovation Research and Training | 1 |
| 24 | Mathematical Analysis III | 5 |
| 25 | Approaching Artificial Intelligence and Data Science | 2 |
| 26 | Introduction to Statistics | 1 |
| 27 | Introduction to data science | 1 |
| 28 | Introduction to data science | 1 |
| 29 | Mathematical Statistics II | 4 |
| 30 | Predictive Analysis | 3 |
| 31 | Bayesian statistics | 2 |
| 32 | Business Intelligence | 3 |
| 33 | Doing Data Science | 4 |
| 34 | Probability Theory I | 4 |
| 35 | Introduction to Biostatistics and Epidemiology | 3 |
| 36 | Stochastic Processes | 3 |
| 37 | Mathematical Statistics | 4 |
| 38 | Advanced Algebra and Analytic Geometry 2-1 | 4.5 |
| 39 | Advanced Algebra and Analytic Geometry 2-2 | 5.5 |
| 40 | Statistical Computing | 2.5 |
| 41 | Methods of Data Collection | 3 |
| 42 | Operatioms Research and Optimization | 3.5 |
| 43 | Nonparametric Statistics | 3 |
| 44 | Regression Analysis | 3 |
| 45 | Design of Experiments | 3 |
| 46 | Data Mining and Machine Learning | 4 |
| 47 | Mathematical Analysis 3-1 | 5.5 |
| 48 | Mathematical Analysis 3-2 | 4.5 |
