| 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. 何謂資料科學
2. 如何根據問題與資料特性選用合適的分析工具
3. 建立利用Python處理資料的基礎能力
4. 培養修習進一步課程的基礎能力 |
| 3.Professional Knowledge in Statistical Analysis |
| 7.Mathematical and Statistical software skills |
|
|
| Networking / Distance Education |
| Exercises |
| Discussion |
| Lecturing |
|
| Internship |
| Assignment |
| Quiz |
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
Introduction |
| Week 2 |
Introduction to Pandas |
| Week 3 |
Explorative data analysis |
| Week 4 |
Linear regression |
| Week 5 |
Linear regression |
| Week 6 |
Cross validation |
| Week 7 |
第一次期中考 |
| Week 8 |
Binary classification |
| Week 9 |
Binary classification |
| Week 10 |
Multiclass classification |
| Week 11 |
Multiclass classification |
| Week 12 |
第二次期中考 |
| Week 13 |
Gradient descent algorithms |
| Week 14 |
Clustering |
| Week 15 |
Novelty detection |
| Week 16 |
期末考
自主學習：artificial neural networks
自主學習：deep learning |
self-directed
learning |
|
|
| Evaluation |
期中考 33%*2
期末考 34% |
| Textbook & other References |
Timbers et al., Data Science: A First Introduction with Python.
Lau et al., Principles and Techniques of Data Science.
McKinney, Python for Data Analysis (3rd ed.)
Grus, Data Science from Scratch (2nd ed.)
|
| Teaching Aids & Teacher's Website |
| iLearning |
| Office Hours |
| TBA |
| Sustainable Development Goals, SDGs(Link URL) |
| include experience courses：N |
|