Key Information
Tutors: Dr Jesús Urtasun Elizari
Course Level: Level 1
Course Credit: 1 credit
Prerequisites: Knowledge of basic statistical concepts.
Duration: 3 x 2 hour session
Course Resources
This course provides an introduction to the statistical theory of sampling, parameter estimation and hypothesis testing. The class is taught on whiteboard to properly introduce the theoretical and mathematical concepts, followed by a series of exercises either with Python or R (see the dates below for details). However, no prior programming experience is required.
Syllabus:
- Fundamentals of probability theory, random variables and distributions
- Sampling from a distribution, the central limit theorem
- Momenta of a distribution (mean, variance, skewness, kurtosis)
- Confidence intervals
- Introduction to hypothesis testing
Learning Outcomes:
On completion of this workshop you will be able to:
- Identify different statistical distributions
- Recognise sampling constraints and variability
- Employ skills to build confidence intervals
- Apply correct test statistics for hypothesis testing
- Assess numerical results to make statistical inferences
Dates & Booking Information
- Monday 09 December 2024 (Part 1), Thursday 12 December 2024 (Part 2) & Friday 13 December 2024 (Part 3), 13:30-15:30, South Kensington (In-Person Teaching)
- Monday 24 March 2025 (Part 1), Tuesday 25 March 2025 (Part 2) & Wednesday 26 March 2025 (Part 3), 10:00-12:00, South Kensington (In-Person Teaching)
- Monday 09 June 2025 (Part 1), Wednesday 11 June 2025 (Part 2) & Friday 13 June 2025 (Part 3), 13:30-15:30, South Kensington (In-Person Teaching)
To book your place, please follow the booking process advertised on the main programme page