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data analysis & predictive modeling course data analysis machine learning bigdata predictive modeling data scientist deep learning r k-means clustering hyper parameters hadoop datasets neural networks clasification python tableau sas functions sas programs sas business analytics data mining arima forecasting trends & forecasting svm kernal data cleaning & audit data visualization objective & scope database kpis qlikview ruby background sql control charts multivariate analysis & segmentation data visualizations dash boards graphs tracking basic metrics presenting data tableau options data sanitization data validation clutsre analysis data exploration r basics need of bigdata stationarity ar process ma process goodness of fit data sources bigdata sources big data baby hadoop meetup understanding data benchmark analysis pca fa overall summary & summary by various segments learning driver analysis gradient boosting boosting boosting algorithm r code r code options statinfer learning rate regularization tensor board model selection cross validation k-fold cross validation 10-fold cross validation bootstrap cross validation sensitivity specificity f1 score roc auc over fitting under fitting bias variance bias variance tradeoff artificial intelligence data analytics time series analysis testing of hypothesis case study t-test p-value step by step learning risk analytics credit risk waterfall analysis variable selection vintage analysis model validation logistic regression model building r data r functions r packages entropy decision tree information gain back propagation gradient descent ai code ann gbm