Course Calender

Course Calender

CUSA1026 Statistical Modeling and Big Data Analytics 統計模型及大數據分析
Course Outline:
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(Last update on: 26 March 2023)

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Key facts for Summer 2023:

Date:   28, 29, 30* August 2023 (14 hours)
Time:   09:30am – 01:00pm; 02:00pm – 05:30pm
Venue:   CUHK campus
Enrollment:   30
Expected applicants:   Students who are studying S4-S5with good knowledge in mathematics
Tuition Fee:   HKD 2,940.00
Lecturer:   Dr. LIU, Kin Yat
* This date is reserved for make-up classes in case there is any cancellation of classes due to unexpected circumstances.



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, evaluate model performance and perform model predictions. Topics include exploratory data analysis, time series models, hidden Markov models, Poisson process and analysis of big data problems. Students will gain hands-on experience in statistical programming at the computer lab.

各種領域的數據(如氣候學,金融及運動)會展示不同的特質。本課程目標是透過統計軟件R去透視數據多方面的特性,從而用適當的統計模型去解釋,評估模型的表現及作出數據預測。本課程涵蓋範圍包括:探索性數據分析,時間序列模型,隱馬爾可夫模型,泊松過程和大數據問題的分析。學生將親身體驗統計程式的編寫。

 

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 promoting to or studying S4 – S6
Organising period:   Summer 2019; Summer 2020; Summer 2021; Summer 2022; Summer 2023
Application method:   SAYT Online application