Spring 2027 data science training courses: January to June

If you plan to learn R or Python in the new year, book before the end of November. Our spring 2027 data science training courses run from January to June, and early bird pricing on the first one ends on 29 November.

There are 22 courses on the schedule. All of them run live online, and the trainers are data scientists and engineers who spend the rest of their week on client projects. Each course runs over two afternoons, 13:30 to 17:00 UK time. The Bayesian inference course is the exception: it runs over four.

Three routes through our spring 2027 training courses

The courses are scheduled so you can take them in order. There are three obvious routes, each ending with a set of skills you can use at work the following week.

Learning R from scratch

Start with Introduction to R on 11–12 January. Data Wrangling in the Tidyverse follows on 25–26 January, then Data Visualisation with ggplot2 on 1–2 February and Programming with R on 8–9 February. That gets you from your first script to writing your own functions in a month.

Why take this route: Clean, chart and automate your data in code. Monthly reporting becomes a script you rerun in minutes, with fewer manual errors.

January too soon? The same four courses run again between 12 April and 11 May. If you are still deciding whether R is worth the time, we wrote about why learning R is a good career move.

Learning Python

Introduction to Python runs on 15–16 February. Programming with Python follows on 22–23 February, and Data Visualisation with Python on 1–2 March. The same three run again in May and June.

Why take this route: Analyse data in pandas, automate repeat tasks and produce report-ready charts, in the same language as your engineering team. It is also the groundwork for machine learning in Python.

Statistics and machine learning in R

This route is for people who already use R. Statistical Modelling with R on 8–9 March covers hypothesis testing, regression, clustering and principal components analysis. Machine Learning with Tidymodels on 15–16 March and Advanced Machine Learning with Tidymodels on 22–23 March take you through pre-processing, model fitting and tuning.

Why take this route: Build, validate and tune predictive models in tidymodels, and explain which variables drive them. Your team moves from reporting what happened to forecasting what happens next, with evidence for how far to trust it.

Two courses that run once this spring

Introduction to Bayesian Inference using RStan runs over four afternoons, 18–21 January. You write Stan programs for a range of statistical models and learn to read the MCMC diagnostics in R. It is the only date for it in the first half of 2027.

Reporting with Quarto runs on 21–22 June. You build reports, slides and documents straight from R or Python code, so the numbers in the report change when the data does. It pairs well with the ideas in our post on reproducible analytical pipelines.

Early bird deadlines

Early bird pricing ends about six weeks before each course. Booking closes a week before the start date. The first three deadlines are:

• 29 November for Introduction to R
• 6 December for Bayesian inference
• 13 December for Data Wrangling in the Tidyverse

Dates for the second half of 2027 will follow.

Using this year’s training budget

If your team has training budget left this December, booking a January course now spends it before the year ends. It also gets you the early bird price, because every January deadline falls before Christmas.

What you get on every course

• PDF notes and scripts to keep
• Live demonstrations and hands-on exercises
• Time with the trainer to ask questions about your own work

We have run more than 1,000 courses, for organisations including NHS Scotland, Shell, Wessex Water and the Royal Statistical Society.

Training a whole team

Every course in our full catalogue can also run privately for one team, using your data and your tools. Group bookings and returning clients get a discount. Email training@jumpingrivers.com with the size of the team and what they need to learn.

See the full public schedule and book a place.