February Training Update

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We have a great selection of online public training courses coming up over the next two months, including a variety of R courses, as well as some more stats-heavy courses on Bayesian Inference and a series of courses by guest trainer Prof. Darren Wilkinson. Read on for a taste of what’s in store, or head over to our training page for full details and to book!

Bayesian Inference

Our upcoming courses on Bayesian inference take you from an introduction through to implementing models using Stan with R.

Introduction to Bayesian Inference

Course level: Foundation

Next course date: 20th February 2023

The capturing and quantification of uncertainty is a very important aspect of model-fitting and parameter inference. Bayesian inference represents a fully-probabilistic approach to parameter inference, allowing a practitioner to quantify their uncertainties through probability densities. However, fitting models in a Bayesian framework can be an involved and complicated affair, often necessitating the use of Markov chain Monte Carlo (MCMC) algorithms and their programmatic implementation.

Introduction to Bayesian Inference using RStan

Course level: Intermediate

Next course date: 20th-23rd February 2023

Despite the promise of big data, inferences are often limited by its systematic structure. Only by carefully modelling this structure can we take full advantage of the data. Stan is a platform for facilitating this modelling, providing an expressive modelling language to implement state-of-the-art algorithms, to draw subsequent Bayesian inferences.

The course will teach participants how to interface with Stan through R!

Whether you want to start from scratch, or improve your skills, Jumping Rivers has a training course for you.


If you already have the basics of R down, and want to get a bit more adventurous with it, take a look at some of our more advanced R courses for plotting and data wrangling. We also offer a course on R best practices, so you can make sure your code stands up to the tests of time.

Data visualisation with ggplot2

Course level: Intermediate

Next course date: 6th-7th March 2023

Want to learn how to effectively visualise your data in R using the elegant {ggplot2} package? With {ggplot2} it’s easy to customise everything from plot layouts and themes to scales, colours, and more! This course will comprehensively take you through basic plot types such as bar and line charts as well as cover more advanced topics such as interactive graphics with {plotly}.

R Best Practices

Course level: Intermediate

Next course date: 20th-21st March 2023

So you can write code? Great. But can you write code which is easy to read, simple to maintain, and reproducible? Under the pressure of deadlines even the best of us can fall victim to bad-practices. In this course we motivate the importance of good-practices, and show how we can make best practices second nature by incorporating them into our normal workflow.

Data Wrangling in the Tidyverse

Course level: Foundation

Next course date: 27th-28th March 2023

If you work with data, you probably spend a lot of time cleaning it and wrangling it into the correct shape. This course will show you how you can use R to efficiently clean and wrangle your data into a format that’s ready for analysis. You will learn about the Tidyverse, what tidy data really is, and how to practically achieve it with packages such as {dplyr}, {tidyr}, {lubridate} and {forcats}.


We are very happy to announce that Prof. Darren Wilkinson is running a series of four courses for data science and statistics with Scala.

Introduction to Scala and Functional Programming

Course level: Advanced

Next course date: 20th March 2023

Course 1 will begin with an introduction to the Scala language and basic concepts of functional programming, as well as essential Scala tools such as Sbt for managing builds and library dependencies. A brief introduction to the IntelliJ IDE will also be provided. The main emphasis will be on the latest version of Scala, Scala 3, but Scala 2 will also be discussed. The course will continue with an overview of the Scala collections library, including parallel collections, and we will see how parallel collections enable trivial parallelisation of many algorithms on multi-core hardware.

Scala for Data Science and Machine Learning

Course level: Advanced

Next course date: 21st March 2023

Course 2 will survey the Scala library ecosystem relevant to data science applications. Particular attention will be paid to Breeze, the Scala library for scientific computing and numerical linear algebra, and Smile, a library for data analysis and machine learning. We will look at reading and writing data, via internet connections and disk, using CSV and other formats. Data manipulation, visualisation/plotting, data summarisation, data analysis and model fitting will each be considered. Documentation libraries (mdoc) and testing frameworks (munit) will also be covered.

Scala for Apache Spark

Course level: Advanced

Next course date: 22nd March 2023

Course 3 will be dedicated to understanding Apache Spark, the distributed Big Data analytics platform for Scala. Spark’s Resilient Distributed Dataset (RDD) will be compared to the parallel collections examined in Course 1, and it will be shown how it can be used not only for the processing of very large data sets, but also for the parallel and distributed analysis of large or otherwise computationally-intensive models. We will see how Spark can be used both interactively and as a Scala library, producing compiled Spark applications for submission to a Spark cluster. We will also cover the use of Spark’s DataFrame for more convenient processing of tabular data.

Statistical Computing with Scala

Course level: Advanced

Next course date: 23rd March 2023

Course 4 will be concerned with the use of Scala for the development of non-trivial statistical applications. We will see how to exploit non-uniform random number generation and matrix computations in Breeze. Both maximum-likelihood and Bayesian statistical inference algorithms will be considered. Monte Carlo methods for simulation and inference will be examined, in addition to optimisation algorithms. As time permits, we will also discuss more advanced FP concepts, such as type-classes, higher-kinded types, monoids, functors, monads, applicatives and streams, and see how these enable the development of flexible and scalable applications in strongly-typed functional languages.

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