This course will teach you how to apply R to all steps of the Data Science Workflow. On the first day we will define data science,learn basicR commands and explore first steps in Workflow, namely Munging and Visualisation. On the second day we will move on to using R on further steps in the Data Science Workflow - training models and testing them. We will do this by applying statistical learning models to various industry-relevant data science problems
- Defining Data Science and Basic R Commands
- Munging with dplyr
- segue: R as a programming language
- Visualisation with ggplot2
- Extending Visualisation with ggvis for interactivity
- Extending Visualisation with flexdashboard
- Communication with R Markdown and Jupiter
- Statistical Learning 101 (A Refresher)
- Predicting Boston House Prices with Linear Regression
- Predicting Credit Card Defaulters with Logistic Regression
- Extending Credit Card Defaulters Predictions with Decision Trees
- Classifying Cancer Subtypes with Support Vector Machines
- Analysing Product Reviews with Sentiment Analysis
- Recommendation Engines with K-Nearest-Neighbours
- Spam Classifiers with Naive Bayes