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.

 

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,分類樹,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. Exercise and Assignment
  3. Lab
  4. Case Discussion
Medium of Instruction:   Cantonese supplemented with English
Assessment:
  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