ST447 Half Unit
Data Analysis and Statistical Methods
This information is for the 2017/18 session.
Dr Yining Chen
This course is compulsory on the MSc in Data Science and MSc in Operations Research & Analytics. This course is available with permission as an outside option to students on other programmes where regulations permit.
Course not available on MSc in Statistics nor on MSc in Statistics (Financial Statistics).
Basic knowledge of probability and first course in statistics
This course will provide an introduction to methods of statistics and data analysis. The statistical software R will constitute an integral part of the course, providing hands-on experience of data analysis. The syllabus will consist of:
Part I - Introduction
- Statistical Software: R
- Data exploration and visualisation
- Probability, random variables and distribution
Part II - Tools of statistical inference
- hypothesis testing
- Bayesian methods
Part III - Regression Methods
- linear regression
- logistic regression
Part IV - Basic time series analysis (topics as time permits)
20 hours of lectures and 10 hours of computer workshops in the MT.
Students will be expected to produce 10 exercises in the MT.
The weekly exercises will enable students to learn how to implement statistical methods in R and provide preparation for the project. They will also ensure that students learn about the different methods of statistics and data analysis, as assessed later on the examination.
All of Statistics, by Larry Wasserman, Springer.
Data Analysis and Graphics using R: an Example-based Appoach, by John Maindonald an John Braun, CambridgeUniversity Press.
Exam (70%, duration: 2 hours) in the main exam period.
Project (30%) in the MT.
The project-based assessment will be via both individual project and group project.
Total students 2016/17: Unavailable
Average class size 2016/17: Unavailable
Controlled access 2016/17: No
Value: Half Unit
Personal development skills
- Team working
- Problem solving
- Application of information skills
- Application of numeracy skills
- Specialist skills