• How to develop algorithms for the statistical analysis of big data;
• Knowledge of big data applications;
• How to use fundamental principles used in predictive analytics;
• Evaluate and apply appropriate principles, techniques and theories to large-scale data science problems.

Section 1: Simple linear regression
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.

Section 2: Modelling data
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.

Section 3: Many models
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.

Section 4: Classification
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.

Section 5: Prediction using models
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.

Section 6: Getting bigger
Set up and apply sparklyr;Use logical verbs in R by applying native sparklyr versions of the verbs.

Section 7: Supervised machine learning with sparklyr
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.

Section 8: Deep learning
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.

Section 9: Deep learning applications and scaling up
Understand some of the ways in which massive amounts of unlabelled data, and partially labelled data, is used to train neural network models;Leverage existing trained networks for targeting new applications;Implement architectures for object classification and object detection and assess their effectiveness.

Section 10: Bringing it all together
Consolidate your understanding of relationships between the methodologies presented in this course, theirrelative strengths, weaknesses and range of applicability of these methods.