What will you learn in Mastering Software Development in R Specialization Course
Learn to build better data science tools using R—from designing data-processing software to packaging your work for others to use.
Gain skills in distributing R packages and creating custom data visualizations to communicate your results effectively.
Program Overview
Course 1: The R Programming Environment
⌛ ~3 hours
Topics: R basics, tidy data concepts, data import, manipulation, text processing, memory, large datasets.
Hands-on: Practice with swirl lessons and data ingestion tasks.
Course 2: Advanced R Programming
⌛ ~4 weeks (≈10 hrs/week)
Topics: Functional programming, debugging, profiling, and object-oriented design in R.
Hands-on: Write robust R functions and debug them.
Course 3: Building R Packages
⌛ ~2 weeks (≈10 hrs/week)
Topics: Package structure, documentation, testing, licensing, version control, CI, cross-platform development.
Hands-on: Build a full R package ready for CRAN submission.
Course 4: Building Data Visualization Tools
⌛ ~4 weeks (≈10 hrs/week)
Topics: Creating visualizations, interactive mapping, grid graphics, custom graphical elements.
Hands-on: Develop custom plotting functions and interactive visuals.
Course 5: Mastering Software Development in R Capstone
⌛ ~3 hours
Topics: Build and document a software package using the NOAA Significant Earthquakes dataset.
Hands-on: Complete capstone modules—data cleaning, geoms, mapping, documentation, deployment.
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Job Outlook
Excellent for aspiring R software developers and tool creators focused on data science workflows.
Perfect for data science engineers, R package authors, and professionals building reusable analytical tools.
Adds value if you’re involved in developing shared tools or dashboards used across teams or projects.
Specification: Mastering Software Development in R Specialization
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FAQs
- No prior R experience is required.
- Designed for beginners interested in R software development.
- Starts with R basics and progresses to advanced topics.
- Swirl lessons and hands-on exercises ease learning.
- Builds confidence before tackling software packaging and visualization.
- Focuses on building reusable R tools, not just analysis.
- Teaches package development, documentation, testing, and deployment.
- Covers functional programming and debugging techniques.
- Includes custom data visualization tool development.
- Emphasizes real-world software engineering practices.
- Yes, the course guides you to build a full R package.
- Covers package structure, testing, licensing, and version control.
- Teaches continuous integration and cross-platform development.
- Includes hands-on assignments for practical experience.
- Prepares learners to share R tools with the community.
- Builds skills for R software development roles.
- Supports creating tools for analytics teams and data scientists.
- Adds value for roles in data science engineering and dashboard development.
- Helps in building reusable analytical tools for organizations.
- Strengthens portfolio with practical, real-world R projects.
- Build and document a software package using real-world datasets.
- Practice data cleaning, visualization, and mapping.
- Apply package development, documentation, and deployment skills.
- Integrate lessons from previous modules into a complete project.
- Portfolio-ready project demonstrates practical R software mastery.
