Place: Conference Room
Introduction to Data Science via Bayesian Computational Methods using R
Date : 18-20 July 2017
Time : 9.30 am - 5.00 pm
Vanue : Conference Room (Level 7)
Historically, scientific interpretation of data using Bayesian methods was successively applied by the popular computer scientist and mathematician Alan Turing during the Second World War which remained secret until recently. Typical data science tasks begin with formulating a series of hypothesis from the domain or often it is a prediction problem. As a data scientist, we would like to make a probabilistic statement about the hypothesis or the target variable we want to predict. The Bayesian methodology provides nice probabilistic setup which enables the user to make a probabilistic statement about unknown target variable, target parameter or hypothesis. Also, there exists a range of socio-economic and business problems where Bayesian model seems an obvious choice. The bottle-neck of Bayesian methodology often turns out to be solving the high-dimension integration problem. The advancement of computational facility and open source software like R provides a platform where high-dimension integration can be solved via MCMC technique. This course will provide you a brief introduction to all such essential data science ingredients.
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