DATA ANALYTICS
Data analytics refers to the skills, technologies, applications and practices for developing new insights and understanding of performance based on data and statistical methods. Businesses increasingly use the services of data scientists to extract meaning from data in order to make better decisions. In the Harvard Business Review article, “Data Scientist: The sexiest job of the 21st century” Thomas H Davenport and D J Patil, say that “what data scientists do is make discoveries while swimming in data. It’s their preferred method of navigating the world around them. 
Linear Methods for Regression and Classification: Overview of supervised learning, Linear regression models and least squares, Multiple regression, Multiple outputs, Subset selection , Ridge regression, Lasso regression , Linear Discriminant Analysis , Logistic regression , Perceptron learning algorithm.


Additive Models, Trees, and Boosting: Generalized additive models, Regression and classification trees , Boosting methodsexponential loss and AdaBoost, Numerical Optimization via gradient boosting ,Examples like Spam data, California housing , New Zealand fish, Demographic data.


Supervised Learning:Fitting neural networks, Back propagation, Issues in training NN, SVM for classification, Reproducing Kernels, SVM for regression, Knearest –Neighbour classifiers( Image Scene Classification)


Unsupervised Learning and Random forests: Association Rules, Cluster Analysis, Principal Components, Random Forests and Analysis. Assessing Performance of a classification Algorithm (ttest, McNemar’s test, Paired ttest, paired Ftest), Analysis of Variance, Creating data for analytics through designed experiments.

Participants must have Basic knowledge Database Management Systems 
For Students: Rs. 8,000/
For Industry sponsored candidates: Rs. 10,000/

