Data Science: Introduction to Sampling & Hypothesis Testing

Key Information

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

Course Resources

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.

Syllabus:

  • random variables and distributions
  • sampling distribution 
  • central limit theorem
  • standard deviation versus standard error
  • confidence intervals
  • 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

  • Tuesday 01 February 2022, 09:30-12:30, MS Teams - Python Version
  • Wednesday 09 March 2022, 09:30-12:30, MS Teams - R Version
  • 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.