Statistics Courses in Cambridge

• Part IA Probability, lectured by Prof Richard Weber: basic concepts in probability, discrete and continuous random variables, inequalities and limits
• Part IB Statistics, lectured by Prof David Spiegelhalter: basic concepts in statistics including maximum likelihood estimators, confidence intervals, hypothesis testing, contingency tables, goodness-of-fit tests and linear models
• Part II Statistical Modelling, lectured by Dr Rajen Shah: introduction to R, linear models, ANOVA, generalised linear models (GLM), binomial regression and poisson regression
• Part II Mathematics of Machine Learning, lectured by Dr Rajen Shah: statistical learning theory, empirical risk minimisation, popular machine learning methods
• Part III Preparation Resources for Statistics
• Part III Modern Statistical Methods, lectured by Dr Rajen Shah: Ridge regression, principal component analysis (PCA), cross validation, support vector machines (SVM), kernels, LASSO, graphical modelling, causal inference and multiple testing
• Part III Statistical Learning in Practice, lectured by Dr Alberto Coca: GLM for regression and classification, model selection and regularisation, mixed effect models, linear discriminant analysis (LDA), support vector machines (SVM), neural networks, time series, practicals in R
• Part III Causal Inference, lectured by Dr Qingyuan Zhao: principles of causal inference, randomised experiments, path analysis and SEM, DAG models, counterfactual causal models, causal identification, observed confounders, instrumental variables (IV) and other topics
• MRC Biostatistics Unit Short Courses: the MRC Biostatistics Unit run a number of successful courses in statistics on a range of topics at different levels, for statistical, clinical and other audiences.

Computing

The University Training website offers various courses on R, Python, SPSS, Stata, etc.

The statistical programming language R can be downloaded from here. Rstudio is a very useful editor for R. It can be downloaded from here. The CRAN contributed documentation page and the R Project documentation page both contain excellent manuals, tutorials, etc. provided by users of R.

Books and Online Resources

• The Elements of Statistical Learning (T. Hastie, R. Tibshirani and J. Friedman) is an excellent book in the field of statistics, data mining, machine learning and bioinformatics. The emphasis is on concepts rather than mathematics and many examples are given in this book.
• An Introduction to Statistical Learning (G. James, D. Witten, T. Hastie and R. Tibshirani) provides a broad and even less technical treatment of key topics in statistical learning. Each chapter includes an R lab. This book is appropriate for anyone who wishes to use contemporary tools for data analysis. 
• STATSREF: Statistical Analysis Handbook is a free, web-based statistical analysis resource. It provides a comprehensive guide to statistical concepts, methods and tools, with many examples being provided using a variety of software tools such as R, MATLAB and SPSS.
• There are many youtubers who upload short videos explaining statistical concepts and procedures: StatQuest with Josh Starmer, Dr Nic's Maths and Stats, Brandon Foltz, etc.