AMS1301 - Foundations of Data Science
This module is designed to provide students with the basic concepts and theories involved in statistical methods. Major topics include organisation and presentation of data in an informative way, some useful probability distributions used in statistical inference, estimation of parameters and hypothesis testing.
Textbooks
- Freund, J. E., & Perles, B. M. (2007). Modern Elementary Statistics (12th ed.). Pearson Prentice Hall.
Recommended Books / Reading
- D. R. Anderson, D. J. Sweeney, T. A. Williams, J. D. Camm, J. J. Cochran (2017). Statistics for Business and Economics (13th ed.). Mason: South-Western Cengage Learning.
- Hogg, R. V., & Tanis, E. A. (2009). Probability and Statistical Inference (8th ed.). Upper Saddle River: Pearson.
- Keller, G. (2012). Managerial Statistics (9th ed.). Australia: South-Western Cengage Learning.
- Mario F. Triola (2017). Elementary Statistics (13th ed.). Pearson.
- Mendenhall, W., Beaver, R. J., & Beaver, B. M. (2013). Introduction to Probability and Statistics (14th ed.). Belmont, CA: Brooks/Cole, Cengage Learning.
- Wackerly, D. D., Mendenhall, W., & Scheaffer, R. L. (2008). Mathematical Statistics with Applications (7th ed.). Belmont: Cengage Learning.
Suggested Learning Schedule
| Lecture | Topic | Supplementary Materials |
|---|---|---|
| 1 | Introduction to Data Science Overview of the data science workflow and key concepts. | None |