Module overview
Aims and Objectives
Learning Outcomes
Learning Outcomes
Having successfully completed this module you will be able to:
- Use Python functions built in various libraries to fit and analyse such models to data
- Formulate time series models and construct Python-based versions.
- Produce well-structure assignment reports describing problem formulation and solution
- Appreciate both the capabilities and the limitations of such computer-based techniques
Syllabus
Learning and Teaching
Teaching and learning methods
Type | Hours |
---|---|
Independent Study | 59 |
Teaching | 16 |
Total study time | 75 |
Resources & Reading list
Textbooks
Rob J Hyndman and George Athanasopoulos (2012). Forecasting: principles and practice. Ortexts.com.
Anderson, R.A., Sweeney, D.J. and Williams, T.A. (1994). An Introduction to Management Science. West Publishing Co.
Draper, N.R. and Smith, H. (1981). Applied Regression Abalysis. New York: John Wiley.
Makridakis, S., Wheelwright, S.C. and Hyndman, R.J. (1998). Forecasting: Methods and Applications. New York: Wiley.
Phuong Vothihong, Martin Czygan, Ivan Idris, Magnus Vilhelm Persson & Luiz Felipe Martins (2017). Python: End to End Data Analysis. Packt Publishing.
Gilchrist, W.G. (1976). Statistical Forecasting. New York: Wiley.
Janert, P.K. (2011). Data Analysis with Open Source Tools. Sebastopol: O'Reilly.
Wetherill, GB. (1981). Intermediate Statistical Methods. London: Chapman and Hall.
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
Method | Percentage contribution |
---|---|
Coursework | 100% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
Method | Percentage contribution |
---|---|
Coursework | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
Method | Percentage contribution |
---|---|
Coursework | 100% |
Repeat Information
Repeat type: Internal & External