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Data Science Beginner

R Programming

R for statistics and analysis: vectors, matrices and data frames, then dplyr for manipulation and ggplot2 for plotting, followed by hypothesis testing, regression, time series and exploratory data analysis in RStudio.

30 chapters 2h 30m
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What you'll learn

  • Introduction to R Programming
  • Installing R and RStudio
  • R Syntax and Basics
  • Variables and Data Types in R
  • Operators and Expressions
  • Conditional Statements and Loops
  • Functions in R
  • Vectors in R

Course content

30 chapters · 2h 30m
  1. 1 Introduction to R Programming 5 min
  2. 2 Installing R and RStudio 5 min
  3. 3 R Syntax and Basics 5 min
  4. 4 Variables and Data Types in R 5 min
  5. 5 Operators and Expressions 5 min
  6. 6 Conditional Statements and Loops 5 min
  7. 7 Functions in R 5 min
  8. 8 Vectors in R 5 min
  9. 9 Matrices and Arrays 5 min
  10. 10 Lists and Data Frames 5 min
  11. 11 Working with Strings in R 5 min
  12. 12 File Handling in R 5 min
  13. 13 Data Import and Export 5 min
  14. 14 Data Cleaning in R 5 min
  15. 15 Data Manipulation with dplyr 5 min
  16. 16 Data Visualization with ggplot2 5 min
  17. 17 Statistical Analysis in R 5 min
  18. 18 Probability Distributions 5 min
  19. 19 Hypothesis Testing 5 min
  20. 20 Correlation and Regression Analysis 5 min
  21. 21 Time Series Analysis in R 5 min
  22. 22 Exploratory Data Analysis (EDA) 5 min
  23. 23 Machine Learning Basics in R 5 min
  24. 24 Classification and Clustering 5 min
  25. 25 Working with Real-World Datasets 5 min
  26. 26 R Shiny Basics 5 min
  27. 27 Advanced R Programming Concepts 5 min
  28. 28 R Programming Interview Preparation 5 min
  29. 29 Performance Optimization in R 5 min
  30. 30 Final Projects and Real-World Applications 5 min