Overview

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Take your R programming skills to the next level with this final, practical course that bridges theory and real-world applications. Learn to collect data from web sources, create interactive visualizations, handle large datasets efficiently, and integrate with cloud services. While covering advanced topics, the course maintains a beginner-friendly approach with step-by-step guidance and AI assistance. By the end of this course, you will be able to: Collect and process data from web sources using scraping and API techniques, transforming raw online data into structured, analysis-ready formats. Build interactive visualizations and dashboards that allow audiences to explore data dynamically, going beyond static charts and reports. Integrate R workflows with cloud services and external platforms, enabling you to build end-to-end data pipelines that connect collection, analysis, and delivery.

Syllabus

Web Scraping and Data Collection

In this module, you’ll use one of the most valuable data skills out there: collecting real-time information from the web. You’ll learn how to use tools like rvest and APIs to extract data from websites and online platforms. You’ll learn how to use tools like rvest and APIs to extract data from websites and online services in ways that are practical, efficient, and aligned with ethical standards. You’ll practice turning live websites into structured datasets and set up workflows that save you time and effort. Whether you’re tracking trends, pulling social media data, or automating updates, these skills give you a serious edge.

Geospatial Data and Interactive Visualization

In this module, you’ll build a strong foundation in working with geographic data, an essential skill for mapping patterns, trends, and relationships that numbers alone can’t show. Using the leaflet package, you’ll create interactive maps that go beyond static visuals. You’ll learn how to add markers, layers, and controls that let users explore your data in meaningful ways. Whether you're analyzing locations, tracking movement, or sharing insights with non-technical stakeholders, spatial visualizations can bring your work to life, and this module shows you how to start.

Advanced Data Manipulation with data.table

In this module, you’ll learn how to handle large datasets with speed and precision using the data.table package, one of R’s most powerful tools for high-performance data manipulation. You’ll explore techniques that make your code faster and more scalable, especially for complex tasks that push beyond what traditional tools can handle. Through real-world examples and focused practice, you’ll see why data.table is a go-to choice for working data professionals. If you’re dealing with big data, or want to write code that feels sharp and efficient, this module will show you how.

Advanced RMarkdown

In this module, you’ll take your reporting skills to the next level with advanced features in RMarkdown. You’ll learn how to build dynamic, parameter-driven reports that update automatically as your data changes, saving time and reducing manual effort. From filtering content to customizing outputs, you’ll discover how to create reports that are flexible, reusable, and ready for real-world workflows. If you work with stakeholders, share results, or just want to streamline your reporting process, this module gives you the tools to do it smarter.

Introduction to Cloud Storage and Data Management

In this module, you’ll learn how to work with cloud storage, an essential part of modern data workflows. Using Azure Blob Storage, you’ll practice storing, accessing, and managing data in the cloud. Along the way, you’ll see how cloud-based tools fit into real-world analysis, reporting, and collaboration. Whether you're preparing for larger-scale projects or just want to keep your data work more organized and accessible, this module helps you build the skills to do it with confidence.

Capstone Project and Portfolio Integration

In this capstone project, you’ll take on a real-world data challenge that brings together everything you've learned: web scraping, data manipulation, interactive visualizations, and dynamic reporting. You’ll also create a polished, professional GitHub portfolio to showcase your work. Capstones can feel like a lot, and that’s completely normal. You’ll have support along the way, and by the end, you’ll walk away with something you can be proud of, and share with confidence.