Course Calender

Course Calender

CUSA1026 Statistical Modeling and Big Data Analysis 統計模型及大數據分析

Key facts for Summer 2019:

Date:   12, 14 August 2019
16 August 2019* (makeup class)
Time:   12, 14 August 2019: 09:30am– 12:30pm; 2:00pm–5:00pm
16 August 2019*: 09:30am – 12:30pm; 2:00pm–5:00pm (make up class)
Tuition Fee:   HKD 2,750.00
Lecturer:   Dr. LEE Pak Kuen Philip
Course Outline:   Download HERE


* This date is reserved for make-up class in case there is any cancellation of classes due to bad weather or other unexpected factors.



Data from various fields, such as climatology, finance and sports, exhibit different properties. This course aims to use the R-package (a statistical software) to visualize the properties of the data, fit the data into various statistical models, assess model performance and predict the data. Topics include exploratory data analysis, time series models, hidden Markov models, classification trees, Poisson process and analysis of big data problems. Students will gain hands-on experience in statistical programming at the computer lab.



Organising units:
  • Department of Statistics, CUHK
  • Centre for Promoting Science Education, CUHK
Category:   Category II – Academy Credit-Bearing
Learning outcomes:   Upon completion of this course, students should be able to:
  1. Understand data from various fields
  2. Apply the exploratory data analysis (EDA) to visualize the properties of the data;
  3. Understand the theories behind various statistical models, and how the models can be fitted into
    different data sets;
  4. Write computer programs in R to perform various statistical analysis;
  5. Develop a systematic approach in solving statistical problems;
Learning Activities:
  1. Lectures
  2. Exercise and Assignment
  3. Lab
  4. Case Discussion
Medium of Instruction:   Cantonese supplemented with English
  1. Short answer test or exam
Recognition:   No. of Academy unit(s): 1
* Certificate or letter of completion will be awarded to students who attain at least 75% attendance and pass the assessment (if applicable)
Expected applicants:   Students who are studying in S4-S5 with good knowledge in mathematics
Organising period:   Summer 2019
Application method:   SAYT Online application