Big Data Analytics 

offered by The University of Adelaide

Fit a simple linear regression between two variables in R;Interpret output from R;Use models to predict a response variable;Validate the assumptions of the model.

Adapt the simple linear regression model in R to deal with multiple variables;Incorporate continuous and categorical variables in their models;Select the best-fitting model by inspecting the R output.

Manipulate nested dataframes in R;Use R to apply simultaneous linear models to large data frames by stratifying the data;Interpret the output of learner models.

Adapt linear models to take into account when the response is a categorical variable;Implement Logistic regression (LR) in R;Implement Generalised linear models (GLMs) in R;Implement Linear discriminant analysis (LDA) in R.

Implement the principles of building a model to do prediction using classification;Split data into training and test sets, perform cross validation and model evaluation metrics;Use model selection for explaining data with models;Analyse the overfitting and bias-variance trade-off in prediction problems.

Set up and apply sparklyr;Use logical verbs in R by applying native sparklyr versions of the verbs.

Apply sparklyr to machine learning regression and classification models;Use machine learning models for prediction;Illustrate how distributed computing techniques can be used for “bigger” problems.

Use massive amounts of data to train multi-layer networks for classification;Understand some of the guiding principles behind training deep networks, including the use of autoencoders, dropout, regularization, and early termination;Use sparklyr and H2O to train deep networks.