R Programming for Data Science
At a Glance
This comprehensive course offers an in-depth exploration of R programming for data science, covering essential concepts and techniques for data manipulation, visualization, statistical analysis, and machine learning. Participants will gain practical skills in using R and popular R packages such as dplyr, ggplot2, and caret to analyze and visualize data, perform statistical tests, and build predictive models.
Objectives
By the end of the course, participants will be equipped to:
- Understand the fundamentals of R programming and its applications in data science
- Perform data manipulation and transformation tasks using the dplyr package
- Create informative and visually appealing data visualizations using the ggplot2 package
- Conduct exploratory data analysis (EDA) to gain insightsÂ
- Apply statistical techniques and hypothesis tests to analyze data and make inferences
- Build and evaluate machine learning models for predictive analytics using the caret package
Prerequisite
No prior programming experience is required. This course is suitable for beginners who are new to R programming or looking to learn R Programming for the first time.
Curriculum
- 10 Sections
- 0m Duration
Introduction to R Programming
R Basics and Data Structures
Data Manipulation with dplyr
Data Visualization with ggplot2
Data Import and Export
Exploratory Data Analysis (EDA)
Statistical Analysis with R
Introduction to Machine Learning with R
Model Evaluation and Validation
Implementation of R Programming and Data Science Skills
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