The module teaches the students the fundamentals of writing structured computer programs and good computer programming practice, also focussing on some special features of python which make it especially useful for Data and Decision Science. In its second part, the module introduces the students to time series models and associated forecasting methods and algorithms.
Having successfully completed this module you will be able to:
No prior programming experience is required for the module. The Python part will cover the basic principles of programming in a high-level language. The main focus will, however, be in developing a working facility of Python. The module will cover a range of the most commonly used techniques and algorithms including technical calculations as well as data manipulation, graphics, file handling, and the use of package extensions. Practical exercises are used to reinforce the ideas taught in the module, which will enable the students to build their own library of algorithms for use in other modules or in project work.
In the Forecasting part, the module will cover Time Series Models, Decomposition, Analysis and Removal of Trends and Seasonality, Exponential Smoothing Methods (such as Single Exponential, Holt and Holt-Winters Methods), Simple and Multiple Regression Techniques, and Box-Jenkins Methods for ARIMA models.
The module will be taught using a mixture of lectures and computer workshops. The Python part will be made of 18 hours of computer workshops and 6 hours of lectures. The Forecasting part will have 8 hours of lectures and 8 hours of computer workshops.
| Type | Hours |
|---|---|
| Teaching | 40 |
| Independent Study | 110 |
| Total study time | 150 |
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Coursework | 50% |
| Coursework | 50% |