Data Science and Analytics
Data Science and Analytics Examples
A recent review by the Royal Society has outlined that there is an increasing demand for data-driven skills across the UK. For instance, this report highlights that between 2013 and 2017/18 there has been a 43% increase in the number of advertised data analyst jobs, while listings for data scientists have increased by 1287% in the same period. Given the vast quantities of data which are being generated, there is a need for individuals with analytical skillsets to make sense of this information and in doing so allow organisations or businesses to make better, or more informed, decisions.
As discussed above, data analytics and data science are two disciplines which are increasingly in demand. However, these terms are frequently used interchangeably, yet often require differing skillsets. Indeed, there are various articles which address the differences between data science and data analytics. One such difference regards the tools which are utilised in these fields, with data scientists often implementing various machine learning approaches, while data analysts may employ more commonplace or ‘traditional’ statistical methods.
Within this website I have attempted to showcase and outline a variety of different methods from both data analytics and data science. Some of these approaches may be more familiar than others, but when illustrating these methods, I have attempted to make these introductions easy to understand so that anyone with a relatively basic understanding of statistics may be able to grasp these techniques.
Alongside the explanation of each method, I have also put together an Rmarkdown file which provides a complete walk-through of an example for each approach. All of the code should be able to run on any local machine, however where data is taken from external sources this will always be highlighted.
A list of the different methods, explanations, and tutorials is available from the links at the start and end of this page.