Data Science

To my PhD-oriented research-track students: All the students under my direct supervision are highly encouraged to jointly and constantly get exposure to data science courses and modules as listed below (not limited to). Courses, learning sources, and customized supervision can be made uniquely according to students' academic interests in specific niches of research areas upon open discussion with the supervisor. Modules listed below are collectively adapted from well-regarded programs and publicly available sources to favor my research students' learning and initial searching steps. Necessary textbooks (if available) are posted and being updated gradually in the 'textbooks' section in this Google Site for my research students. I wish my research student an enjoyable learning journey working with the supervisor on hands-on, scholarly output-oriented research projects led by students.

1. Data Science Principles

2. Data Structures

3. Algorithms

4. Machine Learning

5. Probability

6. Statistical Inference

7. Predictive Modeling

8. Statistical Modeling 

9. Decision Analytics

10. Advanced Predictive Analyses

11. Data Exploration 

12. Data Visualization

13. Machine Learning

14. AI Systems 

15. Generative Modeling

16. Deep Learning

17. Natural Language Processing

18. Optimization

19. Reinforcement Learning

20. Computational Statistics

21. Statistical Methods and Data Analysis

22. Python programming 

23. Java programming

24. C++ programming

25. Multivariable Calculus and Complex Analysis

26. Modern Software Concepts and Python Programming

27. Mathematical & Statistical Foundations for Data Science

28. Responsible Data Science

29. Cloud Computing for Data Science

30. Relational Databases

31. Machine Learning

32. Data Mining

33. Data Storage and Management

34. Computational Methods

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