In this module, we will embark on a guided tour of the course layout, covering essential tools and resources like R, RStudio, and course code access. We will also establish our project setup and understand various file formats, culminating in dynamic documentation using Rmarkdown.

In this module, we will delve into fundamental data types and structures in R. From basic types like integers and logical values to complex structures like matrices, arrays, and data frames, we will explore and manipulate these elements to build a solid data science foundation.

In this module, we will explore the core programming constructs in R. We will cover operators, loops, and functions, providing both theoretical understanding and practical experience through labs, enabling us to automate tasks and write efficient code.

In this module, we will learn to handle data import and export processes in R. From fetching data from diverse origins to saving and sharing results, we will also explore web scraping techniques to extract valuable information from online sources.

In this module, we will master essential data manipulation techniques in R. We will learn to construct efficient data pipelines, filter data subsets, aggregate large datasets, and reshape data structures. Labs will reinforce our practical skills through real-world challenges.

In this module, we will explore data visualization techniques using R. From static plots with ggplot2 to interactive visualizations with plotly and leaflet, we will learn to communicate data insights effectively. Practical labs will enhance our skills in creating compelling visual narratives.

In this module, we will tackle advanced data manipulation techniques. We will learn to detect and manage outliers and missing data, ensuring robust analyses. Additionally, we will explore regular expressions for powerful text data manipulation, enhancing our data processing capabilities.