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Data Analytics with R Programming Certification Training Course

Data Analytics with R Programming Certification Training Course Course

Edureka’s Data Analytics with R program delivers a balanced mix of data wrangling, visualization, statistical analysis, and modeling—culminating in an end-to-end capstone that mirrors real-world workf...

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Data Analytics with R Programming Certification Training Course on Edureka — Edureka’s Data Analytics with R program delivers a balanced mix of data wrangling, visualization, statistical analysis, and modeling—culminating in an end-to-end capstone that mirrors real-world workflows.

Pros

  • Hands-on emphasis with real datasets across every module
  • Strong coverage of both static and interactive visualization techniques using Shiny and plotly
  • Comprehensive capstone project that showcases complete analytics workflow

Cons

  • Limited focus on time-series and clustering methods—requires supplemental courses for advanced analytics
  • Assumes basic familiarity with R; absolute beginners may need a rapid primer

Data Analytics with R Programming Certification Training Course Course

Platform: Edureka

What will you learn in Data Analytics with R Programming Certification Training Course

​

  • Perform data import, cleaning, and manipulation in R using readr, dplyr, and tidyr

  • Visualize data with ggplot2: scatterplots, bar charts, histograms, boxplots, and thematic customization

  • Apply statistical analysis: summary statistics, hypothesis testing (t-tests, chi-square), correlation, and ANOVA in R

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  • Build predictive models with linear and logistic regression, decision trees, and random forests using caret

  • Automate 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, ggplot2 aesthetics, scales, facets, themes

  • Hands-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 Markdown

  • Hands-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

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FAQs

Do I need prior HPC or supercomputing experience to take this course?
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.
Will I learn to run parallel programs on HPC systems?
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.
Does the course cover job schedulers like PBS and Slurm?
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.
Can this course help me pursue a career in HPC or computational science?
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.
Will I get hands-on practice with HPC systems and supercomputers?
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.

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