Data in most disciplines are multidimensional in nature, be it field surveys pertaining to demographic details, socio-economic profile and preferences or financial market data, where stocks exhibit joint movement. To understand consumption behaviour, variables ranging from income to expenditure on a diverse basket of goods are collected. Understanding and extracting structures of dependence in higher dimensions requires sophisticated mathematical machinery. The methods can be applied to a wide array of settings where the assumptions on the data hold. The course will cover the following topics and techniques: Theory of Multivariate Normal Distributions, Multivariate Central Limit Theorem, Techniques such as Principal Component Analysis, Factor Analysis, Clustering Methods and Classification techniques.