Data Science with R – Statistical Computing

Module 1: Introduction to R and RStudio

• R overview: statistical computing language and its ecosystem
• RStudio IDE: console, script editor, environment, and plots panels
• R data types: vectors, matrices, lists, and data frames
• Installing and loading packages: CRAN and tidyverse ecosystem

Module 2: Data Manipulation with tidyverse

• dplyr: filter, select, mutate, summarise, and group_by
• tidyr: pivot_longer, pivot_wider, separate, and unite
• Joining datasets: inner_join, left_join, and anti_join
• Pipe operator: chaining dplyr operations with %>%

Module 3: Data Visualisation with ggplot2

• ggplot2 grammar of graphics: data, aesthetics, and geoms
• Chart types: geom_point, geom_bar, geom_line, and geom_boxplot
• Faceting: small multiples with facet_wrap and facet_grid
• Themes and scales: customising colours, labels, and axes

Module 4: Statistical Analysis in R

• Descriptive statistics: mean, median, variance, and standard deviation
• Hypothesis testing: t-test, chi-square, and ANOVA in R
• Correlation and regression: lm() and interpreting model output
• Probability distributions: normal, binomial, and Poisson in R

Module 5: Machine Learning with R

• caret package: unified interface for ML model training and evaluation
• Supervised learning: linear regression, logistic regression, and decision trees
• Model evaluation: confusion matrix, ROC curve, and cross-validation
• Unsupervised learning: k-means clustering and hierarchical clustering

Module 6: Text Mining and NLP in R

• tidytext package: tokenisation, stop word removal, and word frequency
• Sentiment analysis: AFINN, Bing, and NRC lexicons in R
• TF-IDF: term frequency-inverse document frequency with tidytext
• Topic modelling: LDA with the topicmodels package

Module 7: R Markdown and Shiny

• R Markdown: creating reproducible reports combining code and narrative
• Output formats: HTML, PDF, and Word from R Markdown documents
• Shiny: building interactive web applications with R
• Shiny dashboard: reactivity, input widgets, and output renders

Module 8: Capstone Project and Assessment

• Exploratory data analysis: tidyverse data wrangling and ggplot2 visualisation
• Machine learning model: classification or regression with caret
• Shiny app or R Markdown report presenting analytical findings
• Final assessment and course certification

Experience in the Industry Learn from data scientists and statisticians who have used R for statistical modelling, exploratory data analysis, machine learning, and data visualisation in healthcare, finance, and research organisations.

Backgrounds at the Top Our R programming trainers have worked at universities, research institutions, and analytics consultancies where R is the primary tool for advanced statistical analysis and reproducible research.

Clear & Effective Teaching R fundamentals, tidyverse data manipulation, ggplot2 visualisation, statistical analysis, machine learning with caret, text mining, R Markdown, and Shiny are explained with real data science project examples.

Hands-On Learning Focus Students wrangle datasets with dplyr, visualise data with ggplot2, build ML models with caret, perform statistical tests, analyse text, and create Shiny apps through structured hands-on R lab exercises.

Up-to-Date Knowledge Trainers keep content current with the latest tidyverse and tidymodels releases, Quarto as a next-generation R Markdown, and evolving R-based data science and statistical computing best practices.