Statistics Foundations

This series of classes will provide a foundational overview of the statistics needed to successfully navigate a research project. By the end of the course, we aim to help you achieve a secure understanding of the most widely used statistical methods for data analysis and to feel confident explaining them to others (e.g. in the PhD viva). 

Across the eight classes we will look at a variety of topics in descriptive statistics, probability theory, hypothesis testing and modelling. Examples in Python will be provided for each topic, plus focused exercises to help you apply what you have learned.

The course is expected to be most useful to first-year PhD students, though all ECRs are welcome to attend. 

This course is open to Research Degree Students, Postdocs & Research Fellows.

Learning Outcomes

On completion of this workshop you will be able to:

• Describe a data sample using appropriate summary statistics and data graphics
• Understand the theory of random variables and probability distributions, and their applications to real-world examples
• Assess the evidence for a hypothesis about a population, using appropriate hypothesis tests or empirical simulations
• Compare alternative models for a population according to their likelihood. 
• Apply the statistical methods described in the course using Python

Prerequisites:

Basic knowledge of Python is required to attend this course.  No prior knowledge of statistics or probability is assumed. 

How to book

• Early Career Researchers (Research Degree Students, Postdocs, Research Fellows) should book via Inkpath using your Imperial Single-Sign-On.

Please ensure you have read and understood ECRI’s cancellation policy before booking.