ST447      Half Unit
Data Analysis and Statistical Methods

This information is for the 2023/24 session.

Teacher responsible

Prof Qiwei Yao


This course is compulsory on the MSc in Data Science, MSc in Health 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.

This course is NOT available on the following programmes: MSc in Statistics, MSc in Statistics (Research), MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (Research), MSc in Statistics (Social Statistics), MSc in Statistics (Social Statistics) (Research) or LSE-Fudan Double Master's in Financial Statistics and Chinese Economy.

This course has a limited number of places (it is controlled access) and demand is typically high. This may mean that you’re not able to get a place on this course.


Basic knowledge in calculus and linear algebra, as well as a course in probability and statistics equivalent to ST102.

Students who have no previous experience in R are required to take on an online pre-sessional R course from the Digital Skill Lab (

Course content

This course covers most frequently used statistical methods for data analysis. In addition to the standard inference methods such as parameter estimation, hypothesis testing, linear models and logistic regression, it also covers Monte Carlo methods, bootstrap, EM-algorithm, permutation tests, regression based on local fittting, causal inference and false discovery rates. The software R constitutes an integral part of the course, providing hands-on experience of data analysis.


This course will be delivered through a combination of classes, lectures and Q&A sessions totalling a minimum of 30 hours across in Autumn Term. This course includes a reading week in Week 6 of Autumn Term.

Formative coursework

Students will be expected to produce 5 exercises in the AT.

The bi-weekly exercises enable students to learn about the different methods of statistics and data analysis. They also provide students the opportunities to implement statistical methods in R.

Indicative reading

All of Statistics, by Larry Wasserman, Springer.

Data Analysis and Graphics using R: an Example-based Appoach, by John Maindonald an John Braun, Cambridge University Press.


Exam (85%, duration: 2 hours) in the January exam period.
Project (15%) in the AT.

Student performance results

(2019/20 - 2021/22 combined)

Classification % of students
Distinction 40
Merit 28
Pass 29.1
Fail 2.9

Key facts

Department: Statistics

Total students 2022/23: 81

Average class size 2022/23: 28

Controlled access 2022/23: Yes

Lecture capture used 2022/23: Yes (MT)

Value: Half Unit

Guidelines for interpreting course guide information

Course selection videos

Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.

Personal development skills

  • Self-management
  • Problem solving
  • Application of information skills
  • Communication
  • Application of numeracy skills
  • Specialist skills