Course Calender

Course Calender

CUSA1026 Statistical Modeling and Big Data Analytics 统计模型及大数据分析
Course Outline:
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(Last update on: 4 March 2021)

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Key facts for Summer 2021:

Date:   13, 17, 20* August 2021 (14 hours)
Time:   09:30am – 1:00pm; 2:00pm –5:30pm
Teaching Platform:   Face to Face  (The Chinese University of Hong Kong) #
Enrollment:   30
Expected applicants:   Students who are studying S4-S5with good knowledge in mathematics
Tuition Fee:   HKD 2,940.00
Lecturer:   Dr. LEE Pak Kuen Philip
* This date is reserved for make-up classes in case there is any cancellation of classes due to unexpected circumstances.
# This course is offered face-to-face lessons at CUHK campus. It may switch to online teaching in accordance with the pandemic development and the policy of the university.



Introduction:

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.

各种领域的数据(如气候学,金融及运动)会展示不同的特质。本课程目标是透过统计软件R去透视数据多方面的特性,从而用适当的统计模型去解释,评估模型的表现及作出数据预测。本课程涵盖范围包括:探索性数据分析,时间序列模型,隐马尔可夫模型,分类树,泊松过程和大数据问题的分析。学生将亲身体验统计程式的编写。

 

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. Lab
Medium of Instruction:   Cantonese supplemented with English
Assessment:
  1. Short answer test or exam
Recognition:   No. of Academy unit(s) awarded: 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 S4-S5with good knowledge in mathematics
Organising period:   Summer 2019; Summer 2020; Summer 2021
Application method:   SAYT Online application