## ALL TRAINING COURSES

## Mastering the Tidyverse

The tidyverse is essential for any statistician or data scientist who deals with data on a day-to-day basis. By focusing on small key tasks, the tidyverse suite of packages removes the pain of data manipulation.

## Next Steps in the Tidyverse

This training course takes you to the next step of the tidyverse journey. We demystify purrr, take the pain out of string manipulation with stringr, and more!

## Introduction to Python

Python is a powerful, fast programming language that plays well with others, runs everywhere, is friendly and easy to learn. Its a general purpose language in nature, however, it has a large number of packages which allows it to be suitable for a variety of tasks from data analysis to web scraping. Pythons syntax is simple yet elegant making it easy to read and quick to develop in. This course will cover the basics of how the language works looking at the core of components Python language.

## Introduction to R

This is a one day intensive course on R. The course is a mixture of lectures and computer practicals. The main focus will be to introduce fundamental R concepts.

## Advanced Graphics with R

This is a one day intensive course on advanced graphics with R. The standard plotting commands in R are known as the Base graphics. In this course, we cover more advanced graphics packages – in particular, ggplot2 and plotly.

## Automated Reporting (first steps towards Shiny)

Do you want to create interactive documents? Do you want your reports to automatically update when the data changes? Then this course is for you! This course is based on a workshop run by Garret Grolemund (RStudio) and Colin Gillespie (Jumping Rivers) at Strata 2015.

## Interactive Graphics with Shiny

This is a one-day intensive course on the R package **Shiny**. The course will be a mixture of lectures and computer practicals. **Shiny** allows you to create cutting-edge, interactive web-graphics. Regardless of your background, Shiny will enable you to present your data in new and innovative ways.

## Statistical Modelling with R

From the very beginning, R was designed for statistical modelling. Out of the box, R makes standard statistical techniques easy. This course covers standard statistical techniques, such as hypothesis testing, regression and clustering.

## Bayesian Inference using Stan

Despite the promise of big data, inferences are often limited not by the size of data but rather by its systematic structure. Only by carefully modelling this structure can we take full advantage of the data — big data must be complemented with big models and the algorithms that can fit them. Stan is a

## Introduction to Bayesian Inference using RStan

Despite the promise of big data, inferences are often limited not by the size of data but rather by its systematic structure. Stan is a platform for facilitating this modelling, providing an expressive modelling language for specifying bespoke models and implementing state-of-the-art algorithms to draw subsequent Bayesian inferences.

## Advanced R Programming

This is a two-day intensive course on R. The main focus of the course is advanced R programming techniques, such as S3/S4 objects, reference classes and function closures.

## Machine Learning with Python

This two-day intensive course will equip you with the knowledge and tools to undertake a variety of tasks in a standard machine learning analytics pipeline. In addition to techniques such as cross-validation, training and testing our models we will examine a range of model building algorithms for both regression and classification supervised learning problems on top of an introduction to unsupervised learning.

## Python for Data Visualisation

Python has a number of package for the effective creation of graphics to communicate your data insights. This one day course will examine a range of packages for building impactful visualisations.

## Python and Tensorflow

Deep learning is a cutting edge machine learning technique for classification and regression. In the past few years it has produced state-of-the-art results in fields such as image classification, natural language processing, bioinformatics and robotics. This course will cover the main ideas of deep learning, and how to implement it in practice with tensorflow: a software framework for efficient and scalable deep learning. We’ll introduce the high-level keras library, which allows deployment of standard neural networks with just a few lines of code, as well as the details of raw tensorflow, allowing a deeper level of customisation.

## Time Series Analysis with R

Predicting the future is a tough problem. Some things, like lottery numbers, are inherently unpredictable. Others, like air temperatures and rainfall, are reasonably predictable. Time series analysis makes it possible to assess whether or not predictions are possible and, if they are, build a model which can generate informed predictions for the future with realistic estimates of uncertainty.

## An Introduction to SQL (with R)

Every data scientist at some point has to deal with databases. As they quickly realise, incorrect SQL queries can kill performance. This course lays the foundations for a working with databases via R.

## Why use R?

This is a **1/2** day session that gives a overview of where and how R is used. Using a combination of lecture based case studies, and hands-on practicals we’ll cover some of the latest developments in the R world.

## R and Microsoft

This course is a practical introduction to using Microsoft products to take R code that works on your machine and making it work for many people at once. Participants will get an overview of available tools and build solutions in each tool.

## Scala for Statistical Computing and Data Science

This course is aimed at statisticians and data scientists already familiar with a dynamic programming language (such as R, Python or Octave) who would like to learn how to use Scala. Scala is a free modern, powerful, strongly-typed, functional programming language, well-suited to statistical computing and data science applications. In particular, it is fast and efficient and is designed to easily exploit modern multi-core and distributed computing architectures.

## R for Big Data

This course is a one-day intensive practical introduction to dealing with large data sets in R. We’ll cover hardware, programming with Rcpp, out-of-memory datasets and sparklyr.

## Predictive Analytics

This is a two day intensive course on using the R programming language for predictive analytics and machine learning. This course will be a mixture of lectures and computer practicals.

## Programming with R

The benefit of using a programming language such as R is that we can automate repetitive tasks. This course covers the fundamental techniques such as functions, for loops and conditional expressions. By the end of this course, you will understand what these techniques are and when to use them.

## Efficient R Programming

This is a one-day intensive course on efficient R programming. This course will be a mixture of lectures and computer practicals. This course is aimed at anyone who uses R but wants tips and techniques for speeding up their code.

## Building an R Package

This is a one day intensive course on building an package in R. This course will be a mixture of lectures and computer practicals. The main focus will be getting a working R package ready for distribution.

## Spatial Data Analysis with R

As spatial datasets get larger more sophisticated software needs to be harnessed for their analysis. R is now a widely used open source software platform for working with spatial data thanks to its powerful analysis and visualisation packages.

## Survival/Churn Analysis with R

This course is a practical introduction to some of the everyday and more sophisticated tools used for the analysis of survival data.