Analysis of multivariate and high dimensional data Big data poses challenges that require both classical multivariate methods and contemporary techniques from machine learning and engineering. This modern text integrates the two strands into a coherent treatment drawing together theory data computation and recent research. The theoretical framework includes formal definitions theorems and proofs which clearly set out the guaranteed safe operating zone for the methods and allow users to assess whether data is in or near the zone. Extensive examples showcase the strengths and limitations of different methods in a range of cases: small classical data; data from medicine biology marketing and finance; high dimensional data from bioinformatics; functional data from proteomics; and simulated data. High dimension low sample size data gets special attention. Several data sets are revisited repeatedly to allow comparison of methods. Generous use of colour algorithms Matlab code and problem sets complete the package. The text is suitable for graduate students in statistics and researchers in data rich disciplines