Live Online R Programming Course for Data Science

R Programming for Data Science – Live Course Module

 

Total Duration: 5 Weeks (40 Hours)

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Week 1: Foundations of R & Data Handling (8 hrs)

• Session 1 (2 hrs): Introduction to R & Setup

  • Installing R & RStudio, R syntax basics, variables, operators

• Session 2 (2 hrs): Data Structures in R

  • Vectors, Lists, Matrices, Data Frames, Factors, indexing & subsetting

• Session 3 (2 hrs): Data Import & Export

  • Reading/writing CSV, Excel, JSON, SQL, handling missing values

• Session 4 (2 hrs): Practice Session

  • Mini project on dataset cleaning & preparation

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Week 2: Data Manipulation & Transformation (8 hrs)

• Session 5 (2 hrs): Data Wrangling with dplyr

  • filter, select, mutate, arrange, piping with %>%

• Session 6 (2 hrs): Aggregation & Summarization

  • group_by, summarize, window functions

• Session 7 (2 hrs): Data Tidying with tidyr

  • pivot_longer, pivot_wider, reshaping datasets

• Session 8 (2 hrs): Case Study

  • End-to-end data wrangling on real dataset (e.g., COVID-19/Sales)

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Week 3: Data Visualization (8 hrs)

• Session 9 (2 hrs): Base R Plotting

  • plot, hist, barplot, boxplot

• Session 10 (2 hrs): ggplot2 Basics

  • Grammar of Graphics, geom_point, geom_line, geom_bar

• Session 11 (2 hrs): Advanced ggplot2

  • Facets, themes, customization, combining plots

• Session 12 (2 hrs): Visualization Project

  • Build a visual story with real dataset

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Week 4: Statistics & Data Analysis (8 hrs)

• Session 13 (2 hrs): Descriptive Statistics

  • Mean, Median, Variance, SD, correlation & covariance

• Session 14 (2 hrs): Inferential Statistics

  • Hypothesis testing, t-test, chi-square, ANOVA

• Session 15 (2 hrs): Regression Analysis

  • Simple & multiple regression, model evaluation

• Session 16 (2 hrs): Statistical Project

  • Apply regression & hypothesis testing on dataset

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Week 5: Machine Learning & Capstone Project (8 hrs)

• Session 17 (2 hrs): Introduction to Machine Learning with R

  • caret package, Train/Test split, cross-validation

• Session 18 (2 hrs): Classification Models

  • Logistic Regression, Decision Trees, Random Forests

• Session 19 (2 hrs): Clustering & Dimensionality Reduction

  • K-means clustering, PCA basics

• Session 20 (2 hrs): Capstone Project & Presentation

  • End-to-end Data Science project: EDA → Visualization → Modeling