Do you know your Spark from your Shiny? Do you know what a tidyverse is? The RSS has added a raft of new software courses to its training programme to help statisticians gain or improve on their knowledge of statistical software. Programme languages such as R and Python are covered as well as big data platforms such as Hadoop and Spark to manipulate data at scale.
R is a free language and environment for statistical computing and graphics. It is a popular choice for statisticians because it enables a wide variety of statistical tools for data analysis and graphical techniques and is highly extensible. This year, the RSS is offering more courses in R than ever and our 2018 course programme offers a pathway for non-experts to follow a foundation course through to advanced techniques.
For non-experts wishing to learn about the foundations of using R, their first port of call should be our two-day Introduction to R and Regression Models in R course, which focuses on entering, working with and visualising data, as well as regression modelling.
If you already know the basics of R, you might want to look at our courses that focus on performing specific tasks. Our Programming in R course is a two-day intensive course looking at programming techniques such as writing functions and scripts for undertaking bespoke data analysis tasks. R users who want to extend their skills further to cover more advanced topics but do not have a formal background in computing might be interested in our Advanced Programming in R course which covers conditional programming structures and functional programming via a mixture of lectures and computer practicals.
For those interested in applying machine-learning methods to real-world analytics problems, our professional level course, Introduction to Machine Learning in R will explore how machine-learning methodology can help to gain deeper insight from your data, make better decisions and/or build data products.
Our Interactive Visualisation course introduces technologies enabling R users to present their data with interactive visualisations. The first half of the course introduces RMarkdown and htmlwidgets to build interactive charts, maps and data tables which are then published online using RStudio. The second half of the course introduces the basics of Shiny, a framework for creating sophisticated interactive web applications using R.
The RSS is also running an R-related one-day course at its conference in Cardiff, called Mastering the Tidyverse. Tidyverse is a suite of packages designed to make it easier for statisticians or data scientists to improve their workflows by facilitating better connections between data manipulation tools.
It’s not just R that is now being used for data analysis, however. Python is a general-purpose programming language that is now commonly used to perform data analysis. Rob Mastrodomenico’s Introduction to Python course introduces the language, assuming no prior knowledge of it, and will show how to manipulate data using it. This course can be followed up with Handling data with Python, which shows how to import and deal with data in a variety of different forms such as websites (webscraping), API’s and databases. It’s an ideal course for data scientists, statisticians and quantitative analysts who already have a working knowledge of Python.
For those already acquainted with the abilities of the R programming language but are interested in using Python as well, the Python for R Programmers may be of interest. The purpose of this course is to get R users working with Python quickly and effectively and no prior knowledge of Python is needed.
Finally, the RSS has a course for using advanced statistical methods within a big data environment. The two-day Big Data: tools and statistical methods course is to train statistically-minded practitioners in the use of common big data tools, with an emphasis on the use of advanced statistical methods for analysis. The course highlights how processing platforms such as Hadoop and Spark can be used to analyse data at scale.
Booking on our courses can easily be done online and sizeable discounts are available to RSS members with even bigger discounts offered to our professionally accredited fellows (for example, CStat and GradStat). Newly qualified researchers may also be able to apply for bursaries from the NCRM.