| Course Name |
(中) 數據探索的統計思維(6048) |
| (Eng.) Statistical Thinking for Intelligent Data Exploration |
| Offering Dept |
Graduate Institute of Statistics |
| Course Type |
Elective |
Credits |
3 |
Teacher |
TZENG, SHENGLI |
| Department |
Graduate Institute of Statistics/Graduate |
Language |
中/英文 |
Semester |
2026-FALL |
| Course Description |
Effective data analysis begins with understanding the data itself, not with applying algorithms as quickly as possible. This course treats exploratory data analysis as the foundation of statistical thinking rather than a disposable preprocessing step before modeling. Modern analytical tools make it easy to fit models and generate predictions, but statistical thinking cannot be automated: garbage in, garbage out. A model that runs without error is not necessarily informative, and a clean dataset is not necessarily representative. To counter this tendency toward blind reliance on automation, you will develop the habit of questioning data before trusting it, evaluating evidence before accepting results, and thinking independently before drawing conclusions. |
| Prerequisites |
|
self-directed learning in the course |
Y |
| Relevance of Course Objectives and Core Learning Outcomes(%) |
Teaching and Assessment Methods for Course Objectives |
| Course Objectives |
Competency Indicators |
Ratio(%) |
Teaching Methods |
Assessment Methods |
| This course introduces the concepts and methods of modern data exploration. Topics include data summarization, data manipulation, principles and practices of data visualization, sampling bias, missing data, dimension reduction for multivariate continuous or categorical data, and clustering methods for identifying data heterogeneity. |
|
|
|
| Written Presentation |
| Oral Presentation |
| Assignment |
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
Matrix and Projection |
| Week 2 |
Data Summary and Variable Types |
| Week 3 |
Merge and Filter |
| Week 4 |
Univariate and Bivariate Charts |
| Week 5 |
Dimension Reduction for Numerical Data |
| Week 6 |
Missing Data and Biased Data |
| Week 7 |
Dimension Reduction for Categorical Data |
| Week 8 |
Student Presentation 1 |
| Week 9 |
Visualization for Multivariate Numerical Data |
| Week 10 |
Colors |
| Week 11 |
Clustering |
| Week 12 |
Visualization for Multivariate Categorical Data |
| Week 13 |
Good or Bad Charts |
| Week 14 |
Exploring Associations |
| Week 15 |
Exploring Associations |
| Week 16 |
Student Presentation 2 |
self-directed
learning |
02.Viewing multimedia materials related to industry and academia.
03.Preparing presentations or reports related to industry and academia.
|
|
| Evaluation |
Reading Assignment: 26%
Presentation 1: 37%
Presentation 2: 37% |
| Textbook & other References |
Husson, F., Lê, S., & Pagès, J. (2020). Exploratory multivariate analysis by example using R. CRC press.
Wilke, C. O. (2019). Fundamentals of data visualization: a primer on making informative and compelling figures. O'Reilly Media. |
| Teaching Aids & Teacher's Website |
| The iLearning site will host the uploaded materials. |
| Office Hours |
| Tuesday 13:00~15:00 |
| Sustainable Development Goals, SDGs(Link URL) |
| include experience courses：N |
|