Data Science 資料科學

Course Description

This course aims to introduce data science from a pragmatic, practice-oriented viewpoint. Students will learn concepts, R programming language, and tools they need to deal with various facets of data science practice, including data integration, exploratory data analysis, predictive modeling, evaluation, and effective visual communication. By the end of the course, they will be able to apply data science techniques on their own research topics.

Lecture

• Introduction
  • topic 1. Introduction
  • topic 2. Work with R & Documentation and deployment of your code
• Evaluation
  • topic 3. How to evaluate output?
  • topic 4. How to perform evaluation?
• Feature reduction
  • topic 5.1 Feature extraction
  • topic 5.2 Advcanced feature extraction
• Visualization and Exploratory
  • topic 6. Visualization
  • topic 7. Exploring/managing data
• Unsupervised learning
  • topic 8. ​Unsupervised learning
• Supervised learning
  • topic 9. Supervised learning (1/6)
  • topic 10. Supervised learning (2/6)
  • topic 11. ​Supervised learning (3/6)
  • topic 12. ​Supervised learning (4/6)
  • topic 13. Supervised learning (5/6)
  • topic 14. Supervised learning (6/6)
• References and supplementaries
  • Reference Books
  • Additional Material
  • Other References

Notice

The slides are prepared mainly based on the reference books. Please follow their original license if you reproduce. Any questions, please contact Jia-Ming Chang.