Mastering Software Development in R Specialization

A well-structured gateway for anyone looking to build and share data-science tools using R—clear, practical, and focused on real-world development.

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access	Lifetime
level	Beginner
certificate	Certificate of completion
language	English

#Coursera

Description
Additional information

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.

9.7Expert Score

Highly Recommendedx

This specialization offers a focused and practical path for building R-based data science software—especially handy if you want to develop, distribute, or maintain your own R tools.

Value

9.5

Price

9.3

Skills

9.8

Information

9.7

PROS

Taught by Roger D. Peng and colleagues at Johns Hopkins University—top-level credibility behind the curriculum.
Beginner-friendly and flexible—no prior experience required, and it spans approximately 2 months at 10 hours per week.
Teaches high-demand, practical skills: designing data tools, packaging software, and crafting custom visualizations.

CONS

Not ideal if you're looking to learn basic data analysis or statistics—this specialization centers on software development, not data science foundations.
Learner rating is modest at around 4.2/5, which may reflect room for improvement in delivery or pacing.