R Syllabus

by | Jan 31, 2023 | Syllabus

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This comprehensive R programming syllabus is designed for those looking to expand their skills in data analysis, machine learning, and web development using R. The course covers the essential topics in R programming, from setting up the environment and basic syntax, to advanced techniques like deep learning and parallel computing. With a focus on both theory and practical application, this syllabus includes hands-on exercises and a final project that allows students to apply what they have learned. The syllabus includes:

  1. Introduction to R Programming
  2. Data Handling in R
  3. Control Structures and Functions in R
  4. Statistics and Probability in R
  5. Machine Learning in R
  6. R Shiny for interactive web development
  7. Advanced Topics in R
  8. Final Project presentation and discussion.

By the end of this course, students will have a solid understanding of R programming and its applications, making them well-prepared to tackle real-world projects and advance their careers in data analysis, machine learning, and web development.

R Syllabus

  1. Introduction to R Programming
  • Overview of R language
  • Setting up R environment (installation and configuration)
  • Basic syntax and data types
  • Vectors, Matrices, and Arrays
  1. Data Handling in R
  • Importing and Exporting Data
  • Data Cleaning and Pre-processing
  • Data Manipulation (dplyr, tidyr)
  • Data Visualization (ggplot2)
  1. Control Structures and Functions in R
  • Conditional statements (if-else)
  • Loops (for, while)
  • Writing and calling functions in R
  • Anonymous functions and closures
  1. Statistics and Probability in R
  • Descriptive Statistics (mean, median, mode)
  • Inferential Statistics (t-test, ANOVA)
  • Hypothesis Testing
  • Probability distributions (Normal, Binomial, Poisson)
  1. Machine Learning in R
  • Overview of Machine Learning
  • Supervised learning (linear regression, logistic regression)
  • Unsupervised learning (clustering, dimensionality reduction)
  • Model evaluation (cross-validation, confusion matrix)
  1. R Shiny
  • Introduction to R Shiny
  • Building interactive dashboards and web applications
  • Deploying R Shiny apps
  1. Advanced Topics in R
  • Parallel computing and GPU acceleration
  • Time series analysis
  • Text mining and sentiment analysis
  • Deep learning with Keras and TensorFlow
  1. Final Project
  • Using the skills and knowledge acquired during the course to complete a real-world project
  • Presentation and discussion of project results

Note: The syllabus can be adjusted based on the specific needs and preferences of the target audience.

Where to learn R?

You can learn R Programming.



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