Model Online / On-Site
Duration
Modules 5 modules

Course Objective & Scope

Across finance, insurance, biostatistics, market research and econometrics, R remains indispensable thanks to its statistical depth, expressive visualisation grammar and reproducible reporting tools. This course takes participants from core R data structures to end-to-end data analysis and predictive modelling with the modern tidyverse and tidymodels ecosystems.

Participants clean and reshape complex datasets with dplyr and tidyr, craft publication-quality charts with ggplot2, fit statistical and machine learning models, and publish outputs as automated Quarto reports or plumber REST APIs. Teams finish with a reproducible analytics and modelling project built on an organisational dataset.

Who Should Attend?

  • Data scientists and business analysts performing statistical modelling, forecasting and data analysis
  • Risk, actuarial, finance, clinical research and marketing analytics teams
  • Analysts migrating from spreadsheet workflows or legacy R scripts to modern tidyverse/tidymodels practices
  • Prerequisite: familiarity with basic statistics; no prior R experience required

Key Takeaways

  • Work fluently with vectors, factors, lists, tibbles/data.frames and vectorised functions
  • Clean, join and reshape messy enterprise datasets using dplyr, tidyr, stringr and lubridate
  • Create multi-dimensional exploratory visualisations using ggplot2's layered grammar of graphics
  • Build statistical regression/GLM models, time-series forecasts (fable) and ML pipelines with tidymodels
  • Deliver results as parameterised Quarto reports, interactive Shiny apps or plumber REST APIs

Curriculum

R Fundamentals, Data Structures and Functional Programming
  • RStudio/Positron setup, vectors, factors, lists, data.frames/tibbles and indexing
  • Writing functions, the native pipe (|>) and loop-free iteration with purrr
Data Wrangling and Exploratory Visualisation with the tidyverse
  • Reading data via readr/arrow, and filtering, aggregating, joining and pivoting with dplyr and tidyr
  • Layered plotting, faceting and custom corporate themes with ggplot2
Statistical Inference, Regression and Time-Series Forecasting
  • Hypothesis testing, confidence intervals, A/B test evaluation and tidy model outputs with broom
  • Linear and logistic regression (lm, glm) diagnostics and time-series forecasting with tsibble/fable
Machine Learning Workflows with tidymodels
  • Data splitting and cross-validation with rsample, and feature engineering via recipes
  • Model fitting, hyperparameter tuning and evaluation using parsnip, workflows, tune and yardstick
Reproducible Reporting, Environment Management and API Deployment
  • Locking package versions with renv, automated Quarto reports and calling Python via reticulate
  • Interactive dashboards with Shiny and exposing R models as REST APIs with plumber and vetiver

Other Courses in This Category

  • Python for Data Science Python for Data Science

    Hands-on Python training covering data manipulation with Pandas and Polars, visualization with Matplotlib, Seaborn, and Plotly, exploratory data analysis (EDA), and machine learning modeling with Scikit-learn.

    Programming for AI Corporate, Individual
    Live Course
    4 modules
    View
  • Modern C++ Fundamentals and High-Performance AI Applications

    Modern C++ (C++17/20) for AI and high-performance systems: RAII and smart pointers, STL, move semantics, multithreading, SIMD, building Python extensions with pybind11 and the LibTorch/ONNX Runtime C++ APIs.

    Programming for AI Corporate
    Live Course
    5 modules
    View
  • Python Programming Python Programming

    Master Python from scratch: data structures, control flow, functional programming, Object-Oriented Programming (OOP), error handling, file I/O, virtual environments, and Pandas.

    Programming for AI Corporate, Individual
    Live Course
    9 modules
    View
  • Advanced Python for AI and Data Engineering

    Advanced Python for AI and data engineering: type hinting, decorators, memory-efficient generators, asyncio, multiprocessing and the GIL, Cython/Numba acceleration, profiling and packaging. For developers and data scientists.

    Programming for AI Corporate
    Live Course
    5 modules
    View
  • API Integration and Development with Python for AI Projects

    Consuming and building APIs in Python for AI and data projects: HTTP/REST principles, requests and async httpx clients, authentication, retry/rate-limit handling, LLM APIs and foundational service design.

    Programming for AI Corporate
    Live Course
    5 modules
    View
  • Building Modern Backend and AI APIs with FastAPI

    End-to-end modern Python web service development with FastAPI: Pydantic v2, Dependency Injection, async SQLAlchemy database integration, OAuth2/JWT security, pytest and Docker. For backend and AI developers.

    Programming for AI Corporate
    Live Course
    5 modules
    View

Get in touch