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Data Analytics Using R

Course Description

Description

This self-paced, industry-relevant course offers a complete introduction to data analytics using R, one of the most versatile programming languages in the data science ecosystem. Learners will gain hands-on experience with data preparation, visualization, and statistical modeling using popular R packages like tidyverse, ggplot2, dplyr, and caret.

Updated to reflect 2025 analytics trends, the course includes AI-enhanced reporting, R Shiny dashboards, and integration with cloud-based data platforms—preparing learners to apply data analytics across finance, marketing, healthcare, and more.

What You Will Learn

  • Understand the R environment, RStudio, and R syntax basics

  • Import, clean, and manipulate data using tidyverse and dplyr

  • Perform exploratory data analysis (EDA) and create data visualizations

  • Apply descriptive and inferential statistics

  • Build predictive models using caret: regression, classification, clustering

  • Conduct time-series forecasting and trend analysis

  • Automate reporting using R Markdown

  • Create interactive dashboards with R Shiny

  • Integrate R with Excel, SQL, Google Sheets, BigQuery, and AWS

  • Explore 2025 trends like AI-based statistical assistants, generative R reports, and cloud analytics pipelines

Course Curriculum

  • Introduction to R and RStudio

    • Setting up R and RStudio

    • R syntax and programming basics

    • Installing and using packages

  • Data Import & Cleaning

    • Reading CSV, Excel, and database data

    • Cleaning with dplyr, handling NA values

    • Data transformation using tidyr

  • Exploratory Data Analysis (EDA)

    • Summary statistics

    • Using ggplot2 for visual insights

    • Detecting trends, outliers, distributions

  • Statistical Analysis in R

    • Central tendency, variability

    • Hypothesis testing: t-tests, chi-square, ANOVA

    • Correlation and significance levels

  • Predictive Modeling with R

    • Regression models

    • Classification and clustering (k-means, decision trees)

    • Model performance evaluation (AUC, confusion matrix)

  • Time Series Analytics

    • Decomposing trends, seasonality

    • ARIMA modeling basics

    • Forecast visualization

  • Visualization & Reporting

    • Creating insightful dashboards using ggplot2, plotly

    • Automated reports with R Markdown

    • Reproducible and exportable reporting formats

  • Interactive Dashboards with R Shiny

    • Basics of Shiny apps

    • Dynamic user inputs and outputs

    • Deployment and integration

  • R Integration and Automation (2025)

    • Connecting R with SQL, Excel, BigQuery, and cloud services

    • Automating ETL tasks

    • Generative AI tools for summarization and code generation

  • Final Capstone + Certification

    • Analyze a real-world dataset (choose domain)

    • Build an end-to-end report or dashboard

    • 90-minute exam and project review for certification

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Chronolearn

Developer

I am a web developer with a vast array of knowledge in many different front end and back end languages, responsive frameworks, databases, and best code practices

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Title From Date To Date Cost
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This Course Fee:

₹24,999.00

₹50,000.00
Course includes:
  • img Level
      Beginner Intermediate Expert
  • img Duration 10h
  • img Passing Marks 75
  • img Exam Duration 1h 30m
  • img Certifications Yes
  • img Language
      English German Arabic French Spanish
  • img Access 90 days
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