| Unit name | Probability and Statistics |
|---|---|
| Unit code | COMS10011 |
| Credit points | 10 |
| Level of study | C/4 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Dr. Houghton |
| Open unit status | Not open |
| Pre-requisites |
None |
| Co-requisites |
None |
| School/department | Department of Computer Science |
| Faculty | Faculty of Engineering |
The aim of this course is to introduce students to mathematics underpinning statistics, to the methods commonly used to analyse data.
Computer science is the science of computation and data. Our approach to data is typically very sophisticated and algorithm driven, however, it is often different from the approaches used in many of the other disciplines to which computer scientists contribute. In many areas of science and in enterprise, data is analysed using a frequentist approach which has been developed over the last two centuries into a powerful and practical method for understanding and interpreting data, particularly the data that results from experiments, whether the empirical results of scientific experiments or data collected in pursuit of business goals. The aim of this course is to introduce computer science students to these methods and to teach them statistical skills which are both useful as an approach to data and as the language of data commonly employed in science and industry.
At the end of this course the students will:
• Understand the foundations of probability and statistics.
• Be familiar with sampling and sampling bias.
• Know and be able interpret different distributions.
• Make the best use of descriptive statistics.
• Be able to analyse data using classical statistical tests.
• Understand the design and analyse of experiments.
Two hours a week lectures and one hour a week workshop.
90% two hour written exam, 10% coursework.
There will be extensive lecturer supplied notes.
