Getting Started in R

Tue, 21 Jun 2022, 9am – 4pm

A$880/attendee + gst

Learn how to import, visualise and analyse data in R, avoid common pitfalls and work with R objects/packages

Who will Benefit: Business analysts, Data scientists that are new-ish R users. No experience in R, is necessary. Course has largely been developed with input from customers, through experience in providing support, training and consulting.

• Introduction to R
• Accessing Help
• Creating Working Directories for different projects.
• R Language Objects & Classes
• Data Import/Export
• Data Manipulation including Stack, Subset & Merge
  
• Data Analysis & Graphics 
  – Histograms, Box Plots, Bar Charts, Scatter Plots
  – Changing symbols, colours, style of points, axes, range etc
  – Labelling & Identifying Points. Adding Titles etc
  – Multiple Graphs on a single graphsheet.
  – Plotting Subsets of Rows
  – Adding points, lines, legends to existing plots
• Exporting Graphics
  
• Statistical Models in R 
  – Linear Regression
  – Non-linear Regression

Intermediate R

Wed, 22 Jun 2022, 9am – 4pm

A$1,380/attendee + gst

Learn best practices, efficient code for data preparation, create advanced visualisations, more advanced regression models and tree based methods. 

Build validated machine learning models for a numeric target variable.

Course outline:

• Introduction & Preliminaries
• Efficient use of R Language objects & functions
• Programming in R – writing functions
• Advanced Visualisations, including Trellis and ggplot2
  
• Data Science/Predictive Modelling:
  – Multiple regression
  – Stepwise regression
  – Regression tree based methods
  – Accuracy measures
  – Validation

• Unsupervised Learning methods
  – Heirarchical
  – Kmeans

Advanced R

Thu, 23 Jun 2022, 9am – 4pm

A$1,380/attendee + gst

Analyse and visualise large datasets using the latest out-of-memory, big data packages.

Build validated ML classification models, eg for Churn, cross/up-sell, staff retention etc

Course outline:

• Working with Big Data in R
  – Big data graphics, models and manipulation
  
• Classification modelling:
  – Logistic regression 
  – Discriminant Analysis
  – Trees: Bagging, Random Forest & Boosting
  – SVM
  – Accuracy measures
  – Split/Cross Validation
  
• Automation in R, including batch processing and deploying to production
  
• Reporting: R Shiny and R Markdown