- R analyst roles need dplyr, ggplot2, stats tests, and R Markdown — not syntax-only drills.
- Expect 15 core modules plus a phased real-time project with hands-on delivery.
- Pairs with Data Science and Machine Learning paths.
- Book Asmorix demo for R batch counseling.
This R Programming course syllabus covers statistical computing from R basics through tidyverse, visualization, inferential statistics, and introductory Shiny — with an analytics project. Use before R Programming Training in Chennai. Related: Data Science Course Syllabus, Machine Learning Course Syllabus, Tableau Course Syllabus.
Strong R training includes tidyverse data wrangling, ggplot2 visualization, statistical testing, reproducible R Markdown — not syntax print statements alone.
Quick Overview
| Item | Details |
|---|---|
| Course focus | Statistical programming and analytics with R and tidyverse |
| Modules | 15 core modules + real-time project guidance |
| Level | Beginner to intermediate analyst / data science foundation |
| Who it is for | Statistics students, analysts, researchers, and ML aspirants |
| Key outcomes | Wrangle data with dplyr, visualize with ggplot2, run hypothesis tests, deliver R Markdown reports |
| Training options | Classroom and live online batches in Chennai with placement support |
Who Should Follow This R Programming Syllabus?
Suited for statistics graduates, Excel analysts moving to code, and data science aspirants. Math comfort helps; programming beginners start with vectors and data frames.
Module 1: R, RStudio & Reproducible Workflows
- R console and scripts
- RStudio projects
- Package installation
- Working directories
- R Markdown documents
- Reproducibility conventions
- Lab: apply this module in a guided R analytics notebook exercise
- Review common interview and on-the-job scenarios for r, rstudio & reproducible workflows
Module 2: R Language Fundamentals
- Vectors and coercion
- Lists and data frames
- Factors
- Operators
- Control flow
- Vectorised operations
- Lab: apply this module in a guided R analytics notebook exercise
- Review common interview and on-the-job scenarios for r language fundamentals
Module 3: Data Import & Tidy Data
- Readr imports
- Missing value conventions
- Tidyr pivoting
- Data type parsing
- Tidy data principles
- Import audit checks
- Lab: apply this module in a guided R analytics notebook exercise
- Review common interview and on-the-job scenarios for data import & tidy data
Module 4: Data Manipulation with dplyr
- Filter and arrange
- Mutate and transmute
- Group_by summaries
- Joins
- Window functions
- Pipeline readability
- Lab: apply this module in a guided R analytics notebook exercise
- Review common interview and on-the-job scenarios for data manipulation with dplyr
Module 5: Exploratory Data Analysis
- Summary statistics
- Distribution inspection
- Outlier treatment
- Correlation checks
- Segmentation
- EDA reporting
- Lab: apply this module in a guided R analytics notebook exercise
- Review common interview and on-the-job scenarios for exploratory data analysis
Module 6: Data Visualisation with ggplot2
- Grammar of graphics
- Aesthetic mappings
- Faceting
- Scales and themes
- Annotation
- Publication-ready exports
- Lab: apply this module in a guided R analytics notebook exercise
- Review common interview and on-the-job scenarios for data visualisation with ggplot2
Module 7: Statistical Inference
- Sampling distributions
- Confidence intervals
- Hypothesis tests
- P-values
- Effect sizes
- Assumption checks
- Lab: apply this module in a guided R analytics notebook exercise
- Review common interview and on-the-job scenarios for statistical inference
Module 8: Regression Analysis
- Linear regression
- Model diagnostics
- Multicollinearity
- Categorical predictors
- Interaction effects
- Prediction intervals
- Lab: apply this module in a guided R analytics notebook exercise
- Review common interview and on-the-job scenarios for regression analysis
Module 9: Classification & Logistic Regression
- Binary outcomes
- Odds ratios
- Confusion matrices
- ROC curves
- Threshold selection
- Class imbalance
- Lab: apply this module in a guided R analytics notebook exercise
- Review common interview and on-the-job scenarios for classification & logistic regression
Module 10: Time Series Analysis
- Date-indexed data
- Trend and seasonality
- Stationarity
- ARIMA concepts
- Forecast accuracy
- Time-series plots
- Lab: apply this module in a guided R analytics notebook exercise
- Review common interview and on-the-job scenarios for time series analysis
Module 11: Functions, Iteration & Functional R
- User-defined functions
- Apply family
- Purrr mapping
- Scope rules
- Error handling
- Code modularity
- Lab: apply this module in a guided R analytics notebook exercise
- Review common interview and on-the-job scenarios for functions, iteration & functional r
Module 12: Databases & APIs in R
- DBI connections
- SQL translation with dbplyr
- JSON APIs
- Authentication hygiene
- Pagination
- Data extraction logs
- Lab: apply this module in a guided R analytics notebook exercise
- Review common interview and on-the-job scenarios for databases & apis in r
Module 13: Shiny Dashboards
- Reactive expressions
- UI and server separation
- Input validation
- Interactive plots
- Deployment choices
- Shiny performance
- Lab: apply this module in a guided R analytics notebook exercise
- Review common interview and on-the-job scenarios for shiny dashboards
Module 14: Machine Learning with tidymodels
- Train-test splits
- Recipes preprocessing
- Resampling
- Model workflows
- Metric selection
- Model interpretation
- Lab: apply this module in a guided R analytics notebook exercise
- Review common interview and on-the-job scenarios for machine learning with tidymodels
Module 15: R Portfolio & Interview Practice
- Git-based analysis projects
- Methodology narration
- Statistical interview questions
- Code review
- Report presentation
- Career portfolio
- Lab: apply this module in a guided R analytics notebook exercise
- Review common interview and on-the-job scenarios for r portfolio & interview practice
Module 16: Real-Time Project
Analyse a subscription dataset in R and publish a reproducible churn investigation for a product leadership team.
- R Markdown analysis with data dictionary
- Churn model and diagnostic visualisations
- Interactive Shiny summary dashboard
Phase 1
- Import and audit source files
- Define churn outcome and cohorts
- Create exploratory report
Phase 2
- Engineer predictors and fit models
- Compare validation metrics
- Build stakeholder visualisations
Phase 3
- Package R Markdown deliverable
- Create Shiny filters and deployment plan
- Present retention recommendations
How to Use This Syllabus
Ask for tidyverse-heavy labs and R Markdown deliverables. Visit R Programming Training in Chennai with Placement.
Related Courses & Syllabus Guides
Frequently Asked Questions
What does the R Programming course syllabus cover?
It covers RStudio workflow, vectors and data frames, dplyr and tidyr, ggplot2 visualization, descriptive and inferential statistics, hypothesis testing, regression, R Markdown reporting, and an introductory Shiny module.
How many modules are in this R Programming syllabus?
This reference plan has 15 core modules plus a phased real-time project. Institutes may combine or split modules depending on batch duration and lab hours.
Who should follow this R Programming syllabus and what math background is needed?
This syllabus suits analysts, researchers, and data professionals. Basic statistics helps, but modules teach applied tests and interpretation for learners without advanced math.
Is Shiny included in the R Programming syllabus?
Yes. An introductory Shiny module includes a mini interactive app lab, covering inputs, outputs, and reactive patterns.
What projects should be part of R Programming training?
An R Markdown analytics report with exploratory data analysis, statistical inference, and reproducible charts is a strong capstone deliverable.
Is this R Programming syllabus suitable for beginners and career switchers?
Yes. The syllabus starts with R syntax and data manipulation before statistics, so motivated freshers and career switchers can enter analyst roles with guided labs.
How does this R syllabus relate to Python or Data Science training?
R excels at statistics and visualization for research-style reporting. The Python and Data Science syllabi cover a broader general-purpose analytics and machine learning stack.
Where can I join R Programming training in Chennai?
Asmorix offers mentor-led R Programming training with placement support. Book a free demo to match this syllabus to your background and batch timing.