February Training Update | R bloggers

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

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bayesian inference

Our upcoming course on Bayesian inference takes you from an introduction through implementing models using R with Stan.

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Introduction to Bayesian Inference

Course Level: Foundation

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Next course date: 20 February 2023

The capture and quantification of uncertainty is a very important aspect of model-fitting and parameter estimation. Bayesian estimation represents a full-probabilistic approach to parameter estimation, allowing a practitioner to quantify their uncertainties by means of probability densities. However, fitting models to a Bayesian framework can be an involved and complex matter, often requiring 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: 20-23 February 2023

Despite the promise of big data, inferences are often limited by its systematic structure. Only by carefully modeling this structure can we take full advantage of the data. Sten is a platform to facilitate this modelling, providing an expressive modeling language for implementing state-of-the-art algorithms to produce Bayesian inference a posteriori.

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

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


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

Data visualization with ggplot2

Course Level: Intermediate

Next course date: 6-7 March 2023

Want to learn how to effectively visualize your data in R using the elegant {ggplot2} package? With {ggplot2} it’s easy to customize the plot layout and theme, from scale, colors, and more! This course will take you through basic plot types like bar and line charts as well as comprehensively covering more advanced topics like interactive graphics with {plotly}.

r best practice

Course Level: Intermediate

Next course date: 20-21 March 2023

so can you write the code? Great. But can you write code that is easy to read, easy to maintain and reproducible? The pressure of deadlines can make even the best of us fall prey to malpractices. In this course we inspire 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 Tidyverse

Course Level: Foundation

Next course date: 27-28 March 2023

If you work with data, you probably spend a lot of time cleaning and resizing it. This course will show you how to use R to efficiently clean and transform your data into an analysis-ready format. You’ll learn about Tidyverse what tidy data really is, and how to practically achieve it with packages like {dplyr}, {tidyr}, {lubridate} and {forcats}.


We are very pleased to announce that Professor Darren Wilkinson is leading a series of four courses on Data Science and Statistics with Scala.

Introduction to Scala and Functional Programming

Course Level: Advanced

Next course date: 20 March 2023

Course 1 will begin with an introduction to the basic concepts of the Scala language and functional programming, as well as essential Scala tools such as building sbt and managing library dependencies. A brief introduction to the IntelliJ IDE will also be given. 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’ll see how parallel collections enable trivial parallelization of many algorithms on multi-core hardware.

Scala for Data Science and Machine Learning

Course Level: Advanced

Next course date: 21 March 2023

Course 2 will survey the Scala library ecosystem relevant to data science applications. Special attention will be given to Breeze, a Scala library for scientific computing and numerical linear algebra, and Smile, a library for data analysis and machine learning. We’ll look at reading and writing data via an Internet connection and disk, using CSV and other formats. Data manipulation, visualization/plotting, data summary, data analysis and model fitting will each be considered. The documentation library (mdoc) and testing framework (munit) will also be included.

Scala for Apache Spark

Course Level: Advanced

Next course date: 22 March 2023

Course 3 will be devoted to understanding Apache Spark, a 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 that it can be used not only for processing very large data sets, but also for parallelizing and processing large data sets. Can be done for distributed analysis as well. or otherwise computationally-intensive models. We’ll look at how Spark can be used both interactively and as a Scala library to produce Spark applications that are compiled for submission to a Spark cluster. We’ll also cover using Spark’s DataFrames for more convenient processing of tabular data.

Statistical Computing with Scala

Course Level: Advanced

Next course date: 23 March 2023

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

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