Class Details
R Programming Course: Intermediate
Course summary
R is an elegant programming language specifically designed for data science, analytics, and statistics. This Intermediate R Programming course guides participants through more advanced data processing, analytics, and visualisation techniques in R.
By the time you leave our course, you will have a set of data manipulation tools, including tidyverse, dplyr, and ggplot at your fingertips. These tools will help you prepare data for more advanced analytics and modelling. You will also learn to perform statistical regression modelling and hypothesis testing on data sets to uncover valuable patterns and insights. Finally, we will cover programming techniques unique to the R, helping you learn to write optimised and efficient code.
Bringing a combination of private, public, and academic professional experience, your instructor will guide you through Intermediate R, showing you step by step how to utilise R for sophisticated data analytics.
We encourage you to use the CCE R Programming level self-assessment tool if you are unsure which course level to enrol in.
*You are not required to have taken the introductory course – R Programming Course: Introduction. However, you should be familiar with the learning outcomes listed.
Aims
This course aims to provide an in-depth, more advanced coverage of data processing and analysis in the R programming environment. By the end of the day, you should be comfortable processing, manipulating and preparing data for further analysis, visualising data using elegant graphics, computing regression statistics and confidence intervals, and efficiently programming in the R environment.
Learning outcomes
By the end of this course, you should be able to:
- prepare data
- work with advanced R data types like data.frames and lists
- subset data for deep queries
- use the tidyverse suite of packages for data science
- utilise the data.table package for faster processing
- perform analytics and statistical inference
- explore data using ggplot visualisations
- build and analyse linear regression models
- run hypothesis tests and confidence intervals
- produce advanced data visualisations with the ggplot package
- program efficiently in R
- optimise RStudio
- develop for/while loops for data flows
- vectorise R data frames
- write efficient and optimised R code
- utilise custom functions for faster processing.
Content
- The R Statistical Programming Language
- The R Studio Integrated Development Environment (IDE)
- Tidyverse suite of packages for data processing (including dplyr, ggplot2, and more)
- Advanced data types in R
- Data processing and preparation techniques – data subsetting, pipe operators
- R logic flows – for/while loops, vectorizing
- data.table package
- Summary statistic functions
- Linear regression modelling
- Confidence intervals and hypothesis testing
- ggplot for elegant statistical graphics
- Programming efficiency/computational speed checks
- Error handling
Who this course is for
Business professionals, managers, IT knowledge workers and lifelong learners looking for a deeper knowledge of data processing and analytics in R will find Intermediate R Programming helpful.
Prerequisites
This course is aimed at professionals with some basic exposure to the R programming language. Participants of this course should be familiar with the learning outcomes of the R Programming Course: Introduction.
Delivery style
This course is taught through a series of concepts, examples, problem exercises, and in class knowledge challenges. The material is presented so that participants of varying backgrounds, skills and abilities can all move together at a brisk, but comfortable learning pace.
Delivery modes
- Face-to-face, presenter-taught training using your own device
- Online training via the platform Zoom
Materials
A link to access and download the following online course materials (using Dropbox) is provided:
- PowerPoint notes with examples
- all code and script files used throughout the course
- ancillary hand-outs and learning aids.
What you need to do before the course
To participate in this course, you will need R and RStudio installed on your device. They can be downloaded free of charge from:
- Download R here
- Download RStudio here (the free version will suffice)
Face-to-face classes
Please bring your own laptop with all required software pre-installed.
Online classes
For the best experience, we recommend using a computer with a large monitor or dual screens/devices. Small laptop screens can make it difficult to follow the facilitator’s display while working in the software.
Sessions
Location
Face-to-face (CCE Building, Sydney)
Public transport
As bus and train timetables regularly change, we suggest you visit the Transport for NSW trip planner before you set out.
The nearest train stations are Newtown Station and Macdonaldtown Station. Macdonaldtown Station is closer by distance than Newtown Station, however trains do not stop as frequently as at Newtown Station.
Other information
Parking
Nearby parking is available at Wilson Parking - RPA Medical Centre Car Park (entry via Elizabeth Street off Carillon Avenue).
A Flexi Saver Rate is available but must be booked in advance online and includes the booking fee. Bookings can be made via the Wilson website or by downloading the Wilson Parking App. Otherwise, hourly parking rates will apply, as advertised on the Wilson Parking website.
Meet the facilitators
Paul Yacobellis
Paul Yacobellis has been helping professionals optimise their personal productivity for the past 10 years. He has assisted management and individuals implement time saving, energy maximising techniques. He has lead training sessions that cut out wasteful time, reduce stress, and increase overall well-being. Paul’s techniques range from technical to behavioural.
Paul also has experience in data mining and big data analytics, statistical computing, economic/econometric consulting, spatial statistics and Geographic Information Systems (GIS), transport modelling, and business development. He has consulted with government agencies, private corporations and universities, and has previously taught statistics, mathematics, economics and computer science at the University of Central Florida, Orlando.
Paul holds a Master’s in Statistical Computing (Data Mining track) and a Bachelor of Science in Economics and Mathematics.
When not sharing his passion for enhanced personal productivity, Paul works fulltime as data scientist and enjoys parenthood, long-distance running and reading great books.
