Researchers rarely need to become developers. What they need is to import a dataset, run the right test, produce a figure a journal will accept, and repeat it next month when the data changes. This course is built around exactly that, covering data handling, statistics, and reporting in under two focused hours.

This Course Offers

• R and RStudio installation, working directory setup, and getting comfortable with functions and packages
• Data handling covering types, importing, preparing, downloading annual weather data, exporting, and manipulation
• Statistical tests explained as they are used, including descriptive statistics, correlation analysis, ANOVA, Student's t test, and multiple linear regression
• Figures and reports covering basic plots, advanced and animated graphs, wind rose plots across time periods, and generating research reports directly from R
• An introduction to deep learning in R, plus a lecture on the errors that most often stop beginners and how to fix them

Why We Love This Course

1. No prior programming experience is required, since R is taught from installation onwards.
2. It focuses on reproducible analysis, so your work can be re-run and shared rather than rebuilt each time.
3. Publication quality figures and report generation are covered, which is what research actually demands.
4. Dr. Azad Rasul brings a PhD in Geography and over 12 years of programming in research, so the examples come from real academic practice.

Research increasingly expects reproducible analysis, and journals and supervisors notice when figures and statistics can be regenerated. Do you want analysis locked in a spreadsheet you cannot rerun, or code that works again next month? Enrollment is open, and R and RStudio are free.

• This course is perfect for researchers and postgraduate students needing statistics without prior coding experience.
• It suits scientists using SPSS or Excel who want analysis they can re-run and share.
• PhD candidates preparing figures and statistics for a thesis, and environmental or climate researchers working with weather data, will find the practical approach directly useful.

• No prior programming experience is required, since R is taught from installation onwards.
• R and RStudio are needed, both free and available on Windows, macOS, and Linux.
• A dataset of your own is useful but not needed, since the course works with real weather data, and basic statistical familiarity helps but each test is explained as used.

Interested in exploring more lessons? Check out our full course library to continue building your skills and advancing your learning journey.