| Relevance of Course Objectives and Core Learning Outcomes(%) |
Teaching and Assessment Methods for Course Objectives |
| Course Objectives |
Competency Indicators |
Ratio(%) |
Teaching Methods |
Assessment Methods |
1. To help the graduate students build the programming skills needed for data anlysis.
2. To introduce data manipulation, algorithms and algorithmic thinking.
3. To introduce students some modern computer intensive methods in statistical computation and simulation.
|
| 3.Professional Knowledge in Statistical Analysis |
| 7.Mathematical and Statistical software skills |
|
|
|
| Attendance |
| Oral Presentation |
| Assignment |
| Quiz |
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
R graphics |
| Week 2 |
R graphics |
| Week 3 |
Summary data with descriptive statistics |
| Week 4 |
Statistical tests with R language |
| Week 5 |
Statistical tests with R language |
| Week 6 |
Regression Analysis with R language |
| Week 7 |
Regression Analysis with R language |
| Week 8 |
Midterm |
| Week 9 |
Generate discrete random variables |
| Week 10 |
Generate continuous random variables |
| Week 11 |
Statistical analysis of simulated data |
| Week 12 |
Bootstrapping techniques |
| Week 13 |
Introduction to Markov chain |
| Week 14 |
The Metropolis-Hastings algorithm |
| Week 15 |
Final Project Presentations |
| Week 16 |
Final Project Presentations |
self-directed
learning |
02.Viewing multimedia materials related to industry and academia.
|
|
| Evaluation |
| Homework 30%, Midterm 30%, Final Project Presentations 40% |
| Textbook & other References |
https://eprints.uad.ac.id/13/1/Sheldon_M._Ross_-_Simulation.pdf
Sheldon M. Ross - Simulation (4th ed.) |
| Teaching Aids & Teacher's Website |
| 自編講義與教材 |
| Office Hours |
| Appointment |
| Sustainable Development Goals, SDGs(Link URL) |
| 01.No Poverty 02.Zero Hunger 03.Good Health and Well-Being 04.Quality Education 08.Decent Work and Economic Growth | include experience courses：N |
|