Live Course Module: R Course for Data Analytics

Total Duration: 36 Hours (6 Weeks)

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Week 1: Introduction to R and Data Analytics (6 Hours)

• Topics:

  • Overview of R and its role in Data Analytics

  • Installation of R and RStudio environment setup

  • Understanding R syntax and basic data types

  • Variables, operators, and expressions

  • Introduction to R Packages and CRAN

• Outcome:
  Students will be comfortable navigating RStudio, writing simple scripts, and understanding R’s syntax and environment.

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Week 2: Data Structures and Data Manipulation (6 Hours)

• Topics:

  • R data structures: Vectors, Lists, Matrices, Arrays, Data Frames

  • Data importing and exporting (CSV, Excel, JSON, Databases)

  • Data cleaning: handling missing values, duplicates, and outliers

  • String manipulation and date-time handling

• Outcome:
  Learners will be able to prepare, clean, and transform raw datasets for analysis.

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Week 3: Exploratory Data Analysis (EDA) (6 Hours)

• Topics:

  • Descriptive statistics: mean, median, mode, variance, etc.

  • Using dplyr and tidyr for data manipulation

  • Grouping and summarizing data

  • Data aggregation and filtering techniques

• Outcome:
  Students will perform exploratory data analysis and extract meaningful insights from raw data.

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Week 4: Data Visualization with R (6 Hours)

• Topics:

  • Introduction to data visualization principles

  • Base R plotting system

  • Advanced visualization using ggplot2

  • Customizing charts (titles, labels, colors, themes)

  • Creating histograms, scatterplots, boxplots, bar charts, and line charts

• Outcome:
  Learners will visualize analytical findings effectively using R’s visualization libraries.

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Week 5: Statistical Analysis and Modeling (6 Hours)

• Topics:

  • Probability distributions and hypothesis testing

  • Correlation and regression analysis (simple & multiple)

  • ANOVA and Chi-square tests

  • Basic time series analysis introduction

• Outcome:
  Students will understand statistical concepts and apply R for hypothesis testing and predictive analysis.

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Week 6: Real-world Analytics Projects (6 Hours)

• Topics:

  • Case Study 1: Sales Data Analysis

  • Case Study 2: Customer Segmentation using clustering

  • Report generation using R Markdown and dashboards with Shiny

  • Best practices in R for analytics workflow

• Outcome:
  Learners will complete hands-on projects and present analytical reports using real-world datasets.

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🎯 Final Deliverables & Outcomes

• Perform complete data analysis lifecycle in R (import → clean → analyze → visualize → report).

• Build interactive dashboards using Shiny.

• Create reproducible reports using R Markdown.

• Understand core data analytics workflows and apply them in business scenarios.