R for Data Science courses
All courses are instructor led, in person or live-online
Getting Started in R
A$990/attendee + gst
Learn how to import, visualise and analyse data in R, avoid common pitfalls and work with R objects/packages
Day/Time: 9 – 4pm or 9 – 12pm, over 2 days
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
A$1,080/attendee + gst
Learn best practices, efficient code for data preparation, create advanced visualisations, run multiple regression models and tree based methods.
Day/Time: 9 – 4pm or 9 – 12pm, over 2 days
Who will Benefit: Data scientists, quants + relatively new and existing R users.
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
A$1,080/attendee + gst
Analyse and visualise large datasets using the latest out-of-memory, big data packages. Build validated classification models, eg for Churn, cross/up-sell etc
Day/Time: 9 – 4pm or 9 – 12pm, over 2 days
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
