training/
37 pages · Updated June 15, 2026
Pages
- Programming with Python
- Text Mining in R
- Functional Programming with {purrr}
- Machine Learning with Tidymodels
- Data Visualisation with ggplot2
- Reporting with Quarto
- Big Data Analytics with PySpark
- Responsive Web Design in Shiny
- Introduction to Git
- Web Accessibility in Shiny
- Managing Requirements
- Programming with R
- Advanced Concepts in Shiny
- The Power of Databricks Genie Rooms… Data Discovery and Questions with Minimal Effort
- Introduction to Tableau
- Introduction to SQL
- Git for Organisations
- Object-Oriented Programming in Python
- Introduction to R
- Why Use R?
- Managing Packages with Posit Package Manager
- Reporting with R Markdown
- Introduction to Shiny
- Introduction to Python
- Data Wrangling in the Tidyverse
- Data Exploration with Tableau
- Shiny for Python
- LLM-Driven Applications with R and Python
- Shiny Meets LLMs: Smarter App Experiences
- Efficient Data Science in Python
- Self-hosted LLMs: Running Your Own Inference Infrastructure
- Python Best Practices
- Introduction to Machine Learning Operations
- R Best Practices
- Prompt Craft & AI Integration: Building LLM-Driven Workflows in R and Python
- From Nothing to Gold… Productionising with Databricks using the Medallion Architecture
- Improving your workflow with Positron and Claude