Good news! In tandom with the loosening of lockdown restrictions, Jumping Rivers has released the updated 2021 public, online training course schedule. We are offering courses across multiple programming languages, including R, Python, Stan, Scala and git. In the past year, we have converted all of our courses to be online friendly and have recieved great feedback in relation to interactivity, course structure and overall attendee satisfaction. Some examples of feedback we have recieved can be seen below:

“The live coding was well structured and the screen share made it very immersive.”

“I thought the delivery of the content was well presented, and extremely easy to follow”

“Lots of exercises to test knowledge as the course proceeded. Clear explanations for everything. Friendly and engaging presenter.”

Early bird offers are currently avaiable for selected courses and all courses come with a 25% student/academic discount. A summary of the training courses on offer can be seen below:

June:

• Introduction to R: Learn the fundamentals of R and how to import, summarise and plot data using the {tidyverse}.

• Programming with R: Fundamental R techniques such as functions, for loops and conditional expressions.

July:

• Statistical Modelling with R: Learn how to apply statitcial methods such as hypothesis testing, regression analysis, clustering and principal components analysis (PCA).

• Best Practices with R: So you can write code? Great. But can you write code which is easy to read, simple to maintain, reproducible and efficient?

• Introduction to Bayesian Inference: A course on MCMC algorithms, Bayesian workflows and much more!

• Introduction to Bayesian Inference using PyStan: Learn how to apply Bayesian inference/MCMC methods using Python’s interface to Stan, PyStan.

• Introduction to Bayesian Inference using RStan: Learn how to apply Bayesian inference/MCMC methods using R’s interface to Stan, RStan.

August:

• Getting to Grips with the Tidyverse: A {tidyverse} course which focusses on {dplyr}, {lubridate}, {tidyr}, {stringr} and tibbles.

• Next Steps in the Tidyverse: This course examines how/where to use {purrr}, {stringr}, {forcats} and {tidytext} in an analysis.

• Git For Me: A Git course on the importance of tracking project progress via version control.

September:

• Introduction to Python: An introductory Python course on importing, summarising and plotting data..

• Programming with Python: An insight into fundamental Python techniques such as functions, for loops and conditional expressions.

• Python for Data Visualisation: Examining Python packages used for building impactful visualisations that communicate your data insights.

• Scala for Statistical Computing and Data Science: A Scala course outlining how to manage builds and library dependencies; Apache Spark and the Breeze Scala library.

October:

• Python and Tensorflow: Learn the main ideas of deep learning and how to implement them in practice with tensorflow.

• Scala for Statistical Computing and Data Science: A Scala course outlining how to manage builds and library dependencies; Apache Spark and the Breeze Scala library.

• Advanced Graphics with R: Learn the much coveted {ggplot2} package. The {ggplot2} package can create advanced and informative graphics.

November:

• Spatial Data Analysis with R: Discussing how to apply R’s powerful suite of geographical tools to their own problems.

• Reporting with R Markdown: Learn how to dynamically create static/interactive documents; automate the re-generation of these reports with respect to the data in question.

For further information on any of our upcoming courses please visit our public course page.

If you would like to get in touch directly with any queries then please email us at [email protected].

For updates and revisions to this article, see the original post