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