Data Science: Introduction to Sampling & Hypothesis Testing
Tutors: Dr John Pinney
Course Level: Level 1
Prerequisites: Knowledge of basic statistical concepts.
Duration: 2 hour session
Format: Microsoft Teams with live teaching and hands-on practice
This course provides an introduction to the statistical theory of sampling, parameter estimation and hypothesis testing. The class is taught either with Python or R examples (see the dates below for details). However, no prior programming experience is required.
- random variables and distributions
- sampling distribution
- central limit theorem
- standard deviation versus standard error
- confidence intervals
- hypothesis testing
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
- Tuesday 03 May 2022, 09:30-12:30, MS Teams - Python Version
Please email the Graduate School with your CID number to book your place.