This is a short self-study course on hypothesis testing.
Learn more on Hypothesis testing in RThis course is for producers of index numbers or people who want to understand how they are produced.
Learn more on Intermediate index numbersLearners will learn important components used for modelling text. This includes feature representation and language modelling.
Learn more on Intermediate NLP in PythonThis is an introductory session aimed at statistical producers to show how the Code of Practice for Statistics works in practice in the context of the UK statistical system, along with case studies to show how the code has been applied in the past.
Learn more on Introducing the Code of Practice for StatisticsThis short course is designed to introduce learners to Bayesian analysis and how it can be applied within both the R and Python programming languages.
Learn more on Introduction to Bayesian data analysisThis short course is designed to introduce learners to modern software development approaches.
Learn more on Introduction to Continuous IntegrationData linkage provides insight, informs policy change and helps answer society’s most important questions by increasing the utility of data. This self-study course will introduce you to the principles, theory and practice of data linkage.
Learn more on Introduction to data linkageThis course aims to introduce participants to the processes of editing and imputing in surveys.
Learn more on Introduction to editing and imputationThis course will give you an understanding of the git version control system, along with experience working both locally and in collaboration.
Learn more on Introduction to GitIn this course you will learn about a range of machine learning topic areas. You will also learn how to perform the modelling operations using the Python programming language and the package 'scikit-learn'.
Learn more on Introduction to machine learning in Python