My name is Ricardo Campello, I'm a professor in applied mathematics and statistics at James Cook University.

We will learn from the main techniques of data representation, visualisation, pre-processing and analysis, using a modern software package for data science, RStudio and the R programming language.

We will discuss and practice relevant concepts using, not only pedagogy but real data sets, including interesting case studies in domains such as text and network data analytics. Foundational concepts learnt in this subject will be used for most, if not all, subjects in the program.

It's common to hear from experienced data analysts that more than 50 per cent of the time in practical data analysis is spent just by pre-processing and preparing data for analysis. This is so-called data wrangling: selecting, cleaning, transforming and tidying data are essential for a successful data analysis task.

Also, there are multiple ways of representing data and describing the relationships, each of which can be more or less appropriate depending on the application scenario at hand.

Choosing appropriate representations in analysis tools can make the difference between success and failure in your data analysis task.

Students will be able to explain what data science is about, and the areas that play major roles within the realm of data science. They will be able to describe and apply the most common forms of data types and representations.

They will be able to describe and apply a core collection of simple, yet powerful, techniques for data visualisation and exploration; describe and apply core collection of elementary techniques for data pre-processing; interpret, compare and explain results of algorithm complex analysis as well as the importance of this type of analysis in data science.

You will also be able to apply common data representation and pre-processing techniques, such as rangling and tidying, using the software package - RStudio - and the programming language, R.