What will you learn in Data Analytics with R Programming Certification Training Course
Perform data import, cleaning, and manipulation in R using
readr,dplyr, andtidyrVisualize data with
ggplot2: scatterplots, bar charts, histograms, boxplots, and thematic customizationApply statistical analysis: summary statistics, hypothesis testing (t-tests, chi-square), correlation, and ANOVA in R
Build predictive models with linear and logistic regression, decision trees, and random forests using
caretAutomate reporting with R Markdown and Shiny apps for interactive dashboards and reproducible analysis
Program Overview
Module 1: R Environment & Data Import
⏳ 2 hours
Topics: Installing R/RStudio, package management, working directory, data types
Hands-on: Load CSV, Excel, and JSON datasets; inspect with
str(),glimpse(), and summary functions
Module 2: Data Wrangling with dplyr & tidyr
⏳ 3 hours
Topics:
filter(),select(),mutate(),summarize(),group_by(),pivot_longer(),pivot_wider()Hands-on: Clean messy survey data, reshape wide ↔ long, derive new variables
Module 3: Exploratory Data Visualization
⏳ 3 hours
Topics: Grammar of graphics,
ggplot2aesthetics, scales, facets, themesHands-on: Create and customize multi-panel plots to reveal trends and outliers
Module 4: Statistical Analysis in R
⏳ 2.5 hours
Topics: Descriptive stats, confidence intervals, t-tests, chi-square tests, one-way ANOVA
Hands-on: Test differences in group means and associations between categorical variables
Module 5: Predictive Modeling with caret
⏳ 4 hours
Topics: Data partitioning, cross-validation, training linear/logistic regression, decision trees, random forests
Hands-on: Build and compare model performance (RMSE, accuracy), tune hyperparameters
Module 6: Advanced Visualization & Reporting
⏳ 2 hours
Topics: Interactive plots with
plotly, dashboards with Shiny, reproducible reports with R MarkdownHands-on: Deploy a Shiny app showcasing key metrics; generate a PDF report from R Markdown
Module 7: Capstone Project – End-to-End Analytics Workflow
⏳ 4 hours
Topics: Project scoping, data pipeline, analysis, modeling, visualization, and presentation
Hands-on: Execute a complete analytics case study (e.g., customer churn, sales forecasting) and deliver an interactive dashboard
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Job Outlook
Data Analyst: $65,000–$90,000/year — extract insights and build visual reports using R in finance, healthcare, and marketing
Business Intelligence Analyst: $70,000–$100,000/year — develop dashboards and statistical models to inform strategic decisions
Statistical Programmer / R Developer: $75,000–$110,000/year — implement data pipelines, develop Shiny apps, and automate analyses
Specification: Data Analytics with R Programming Certification Training Course
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FAQs
- No prior HPC or supercomputing experience required.
- Covers logging in, data transfer, and environment module usage.
- Introduces hardware and software stacks of HPC clusters.
- Hands-on exercises for job submission using PBS and Slurm.
- Builds foundational skills for scientific and parallel computing tasks.
- Develop parallel code using OpenMP for multithreading.
- Implement MPI programs for distributed-memory communication.
- Write GPU kernels using CUDA for accelerated computation.
- Test performance and speedup for different architectures.
- Combine knowledge for full-stack HPC application workflows.
- Learn PBS commands:
qsub,qstat,qdel. - Learn Slurm commands:
sbatch,squeue,scancel. - Submit batch and interactive jobs on a demo cluster.
- Implement job arrays and resource allocation directives.
- Monitor job status and manage execution efficiently.
- Prepare for roles like HPC User / Research Computing Specialist.
- Gain skills for Parallel Application Developer and Computational Scientist positions.
- Learn to optimize scientific codes with MPI/OpenMP and GPU acceleration.
- Develop reproducible workflows and resource-efficient job scripts.
- Build hands-on portfolio experience for HPC and research projects.
- Connect to a demo HPC cluster and explore nodes.
- Load/unload modules and switch software versions.
- Write, submit, and monitor batch and interactive jobs.
- Parallelize computations using OpenMP, MPI, and CUDA.
- Implement best practices for job scripts and resource allocation.
