Our courses for the second half of 2024 have now been released. We have everything from the very basics of R and Python for data science, to advanced statistical modelling and machine learning. Interested in dashboards and reporting? We have courses on reporting with Quarto, as well as both introductory and advanced Shiny. Already know the basics but want to hone your skills? We have plenty of intermediate courses for you, as well as a course to take a look at some best practices in R and Python.

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

R

Introduction to R

Course Level: Foundation

Upcoming course dates: 3rd July, 7th October

R is a versatile language for statistical computing and graphics. In this course you will learn the advantages of using R and how to get started. You will gain familiarity with the RStudio interface and learn the R basics. Also included is an introduction to the Tidyverse and how to use various packages for data storage, visualisation and manipulation. This course provides a great foundation to begin your R journey!

Programming with R

Course Level: Intermediate

Upcoming course dates: 15th July, 21st October

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. This is a one-day intensive course on R.

Data Wrangling in the Tidyverse

Course Level: Foundation

Upcoming course dates: 10th July, 16th October

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}.

Data Visualisation with ggplot2

Course Level: Intermediate

Upcoming course dates: 22nd July, 4th November

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

Upcoming course dates: 22nd July

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.

Object Oriented Programming in R

Course Level: Advanced

Upcoming course dates: 15th July

The training course will cover R object-oriented programming techniques. We’ll discuss what OOP is and the different varieties within R. Beginning with the popular S3 and S4 OOP frameworks, we’ll finish with the new {R6} package that is used extensively in Shiny applications. By the end of the course, participants will be able to use OOP within their own code.

Shiny

Introduction to Shiny

Course Level: Intermediate

Upcoming course dates: 10th July, 7th October

Do you want to provide interactive visualisation and data exploration features for users who do not have R and data science skills? Discover how easy it can be to use R and {shiny} to create your own apps and dashboards for exploring data without relying on web development or external BI tools. We will show you various examples of input widgets and outputs to display tables and visualisations.

Advanced Concepts in Shiny

Course Level: Advanced

Upcoming course dates: 23rd September, 14th October

Take your interactive {shiny} skills to the next level by creating more robust, responsive and maintainable applications. In this course, we’ll visit more advanced topics that can be used to improve the experience for both those producing the apps and those using them. Subjects will cover: additional ways to react to and validate user inputs; restructuring your app with modules; and an introduction to testing your {shiny} apps.

Python

Introduction to Python

Course Level: Foundation

Upcoming course dates: 9th September, 14th October

Python is a general-purpose programming language popular among data scientists and statisticians. In this one-day introductory course, participants will learn to import, summarise and visualise their data. At each step, we avoid using “magic code”, and stress the importance of understanding what Python is doing.

Programming with Python

Course Level: Intermediate

Upcoming course dates: 16th September, 23rd October

The benefit of using a programming language such as Python 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 how they can be applied to solve real-world data wrangling tasks.

Data Visualisation with Python

Course Level: Intermediate

Upcoming course dates: 17th June, 23rd September, 11th November

Python has a number of packages for the effective creation of graphics to communicate your data insights. This course will examine two popular libraries for creating static 2D plots: Matplotlib and Seaborn. During the training session, we’ll cover plotting basics and customisation of figures with Matplotlib, before moving onto complex statistical visualisations with Seaborn.

Python Best Practices

Course Level: Intermediate

Upcoming course dates: 22nd July

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.

Reporting

Reporting with Quarto

Course Level: Intermediate

Upcoming course dates: 24th June, 23rd September, 18th November

Do you create interactive documents that always need to be updated when the data changes? Then this course is for you. In this course you will learn how to use Quarto to create high quality, dynamic, fully reproducible documents. Quarto is a multi-language open source publishing tool that allows for the creation of dynamic content with Python, R, Julia and Observable.

Machine Learning

Machine Learning with Tidymodels

Course Level: Intermediate

Upcoming course dates: 16th September, 11th November

Machine learning is the process of applying statistical techniques to gain systematic information about a quantity of interest. We will be specifically focusing on how we can use the {tidymodels} suite of packages to implement these techniques. We cover key reasons for model fitting, such as prediction and inference, on quantitative and qualitative responses.

Advanced Machine Learning with Tidymodels

Course Level: Advanced

Upcoming course dates: 23rd September, 18th November

A course that builds on the material covered in our Machine Learning with Tidymodels course. We take a look at how we can fit linear discriminant analysis (LDA) models using {discrim}, assessing model reliability using V-fold cross validation, pre-processing, tree-based models & more. If you wish to explore the abundance of model fitting techniques {tidymodels} has to offer, then this course is certainly for you!

SQL

An Introduction to SQL with R

Course Level: Intermediate

Upcoming course dates: 2nd October

Using databases is a fundamental part of a data scientist’s role. The main focus of this training course is to introduce SQL databases, write your first SQL queries, and show how R can be used to retrieve and manipulate data stored in a relational database. The course uses both the {DBI} and {dbplyr} packages.

We use the PostgreSQL database as an example for public courses. For in-house training, we are happy to adapt the course to match your database requirements.

Introduction to SQL with Python

Course Level: Intermediate

Upcoming course dates: 2nd October

Using databases is a fundamental part of a data scientist’s role. This training course introduces SQL databases and the SQL command syntax, and shows how Python can be used to retrieve and manipulate data held in a relational database. The course also discusses how SQLAlchemy can be used to define and interact with databases using object-oriented Python code.

We use a PostgreSQL database as an example, and communicate with this using a psycopg2 connection.

Statistics

Statistical Modelling with R

Course Level: Intermediate

Upcoming course dates: 9th September, 23rd October

From the very beginning, R was designed for statistical modelling. Out of the box, R makes standard statistical techniques easy. This course covers the fundamental modelling techniques. We begin the day by revising hypotheses tests, before moving onto ANOVA tables and regression analysis. The class ends by looking at more sophisticated methods such as clustering and principal components analysis (PCA).

Introduction to Bayesian Inference using RStan

Course Level: Intermediate

Upcoming course dates: 1st July, 14th October

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

Introduction to Bayesian Inference using PyStan

Course Level: Intermediate

Upcoming course dates: 15th July, 21st October

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 Python!

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