Text analytics is a crucial skill set in nearly all contexts where data science has an impact, whether that be customer analytics, fraud detection, automation or fintech.
In this course, you will learn a toolbox of skills and techniques, starting from effective data preparation and stretching right through to advanced modelling with deep-learning and neural-network approaches such as word2vec.
This course teaches skills for handling both clustering and classification problems and identifies potential pitfalls in both areas, with an emphasis on linking intuition and technique. All sessions are taught in R, with advice given on the packages available and how they can be effectively used.
The course will be led by Presciient director Dr Eugene Dubossarsky or another Presciient instructor.
Dr Dubossarsky is the head of the Sydney Users of R Forum. Eugene is also Principal Founder of Analyst First, an international analytics industry organisation. He is a founder of the Institute of Analytics Professionals of Australia (IAPA); Director, University of New South Wales School of Mathematics and Statistics Industry Advisory Board; and a recognised industry leader in Business Analytics. Eugene is an experienced, analytics professional of 20 years’ experience programming in R and its parent language, S.
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
Meals and refreshments
Catered morning tea and lunch are provided on both days of the course. Please notify us at least a week ahead if you have any special dietary requirements.
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.