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Introduction to R and Data Visualisation: Brisbane, 18–19 April
Tue., 18/04/2017, 9:30 am – Wed., 19/04/2017, 5:00 pm AEST
By booking this course, you agree to our terms and conditions.
For any enquiries, please call 0414 57 33 22.
If you prefer, you can pay by invoice rather than credit card. For instructions, click here.
Introduction to R and Data Visualisation
R is the most popular data mining and statistics package in the world, and it is free to use. It is also easy to use thanks to a range of intuitive graphical user interfaces for statistics, data mining, and interactive visualisation. It is used by a growing number of commercial and government organisations, and is also the tool of choice of elite data mining competition winners. R is open source, flexible, and customisable. Over 4,000 R packages are available as extensions to the base environment, constituting one of the largest and most up-to-date collections of cutting edge Analytics tools in the world. it is also one of the most visually spectacular and universally applicable data visualisation tools.
This two-day course is an introduction to the R programming language, beginning with the most basic operations of downloading and installing the environment. Participants will learn how to input and manipulate data and be instructed in all the aspects of procedural programming in R, allowing them to create their own R functions and customise code. The course will also introduce R data structures, statistical operations, the creation of R visualisations, and options for generating output from all of these to external files. It will also provide an overview of the use of packages in R, and an introduction to some of the most common data mining, interactive visualisation and integrated graphical user interface packages.
Who should attend?
This is a practical course, suitable for existing and prospective data analysis practitioners in government and industry. Participants will be provided with a range of programmatic and user interface options for working with data in R. The course assumes no specialised statistical knowledge. Its focus is developing a practical understanding of R as a tool for business users.
Attendees will, by the end of the course, have the basic skills, resources, guidance and confidence to immediately and self-sufficiently begin to use R in their work.
Having studied stats at Uni I was surprised how far the field has progressed in the last few years, particularly in the area of big data. The great thing about Eugene’s course is I left with a sense that I was up to date with the latest big data modelling concepts but more importantly could also deploy them with some confidence using R. Eugene also made it clear he was available to answer questions after the course, so you are not left hanging. I would absolutely recommend this!
—Damon Rasheed, CEO, Rate Detective
For someone who does not come from an IT background R is a terrifying program. Before doing the Introduction to R course I had previously done other courses in R but always found myself in over my head because they assumed a high level of program experience (even course that required no prior programming knowledge). This course is not like that at all. It starts at ground zero and teaches you everything you need to know to be able to use R confidently in your everyday workplace. It is a must attend for anyone who wants use R!
Data science can be a challenging topic but Eugene’s “Introduction to Machine Learning” course turns complex statistical models into plain English. The course contents and presentation were accessible and I enjoyed the mixture of hands-on rattle() exercises, the challenge of building multiple models with real life data, and the salient theory whiteboard discussions created many “aha" moments.
It was a great introductory course and it gave me with a better grasp of Machine Learning in general, a great framework for thinking about it and practical hands-on skills that I can put to immediate use. I wish I had done this course sooner.
—Charl Swart, Director of Business Operations, Unisys Credit Services
The course assumes no tertiary level training in statistics. Attendees simply need to be familiar with working with structured, electronic data.
Training for all courses will be conducted with Microsoft R Open, the Enhanced Distribution of R. “Predictive Analytics, Machine Learning and Data Science for Big Data” and related courses, along with all advanced courses, will also include use of Azure ML, Microsoft's interactive machine learning platform in the cloud.
Meals and refreshments
Morning tea, lunch, and afternoon tea will be provided.
Please ask about our discounts for group bookings.
Use email@example.com to email us any questions about the course, including requests for more detail, or for specific content you would like to see covered, or queries regarding prerequisites and suitability.
If you would like to attend but for any reason cannot, please also let us know.
Course material may vary from advertised due to demands and learning pace of attendees. Additional material may be presented, along with or in place of advertised.
Cancellations and refunds
You can get a full refund if you cancel 2 weeks or more before the course starts. No refunds will be issued for cancellations made less than 2 weeks before the course starts.
Frequently asked questions (FAQ)
Do I need to bring my own computer?
There’s no need to bring your own laptop or PC. Our courses take place in modern, professional training facilities that have all the computing equipment you’ll need.
I'm lost! How do I find the venue?
Please call 04 1457 3322 or email firstname.lastname@example.org if you can’t find the venue.
Presciient training, coaching, mentoring and capability development for analytics
Please ask about tailored, in-house training courses, coaching analytics teams, executive mentoring and strategic advice and other services to build your organisation's strategic and operational analytics capability.
Our courses include:
- Introduction to R
- Predictive analytics and data science for big data
- Forecasting and trend analysis fundamentals
- Statistics and data analysis
- Forensic data analysis
- Advanced R
- Advanced machine learning masterclass
- Fundamentals of data analysis