Prof Qiwei Yao

This course is compulsory on the MSc in Data Science and MSc in Operations Research & Analytics. This course is available on the MSc in Health Data Science. 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), or MSc in Statistics (Social Statistics) (Research).

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 (https://moodle.lse.ac.uk/course/view.php?id=7745).

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. 

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.

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.