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This course delves into data manipulation, analysis, and visualization with R. It demonstrates the power of R as open-source statistical software, unlocking valuable insights for decision-making across diverse fields.

You'll begin by mastering the basics of R programming, fundamental data structures in R, and file management. You will learn fundamental statistical concepts such as hypothesis testing and regression analysis. You will also learn to leverage well-structured data for creating insightful visualizations using R's robust graphics libraries.

After completing this course, you'll confidently approach data-driven challenges. With proficiency in R, you can efficiently manipulate and analyze data, positioning yourself as a sought-after asset in data-centric roles. Your ability to interpret statistical results and create compelling data visualizations will empower improved decision-making, paving the way for new opportunities for career growth and success across various industries.

Introduction to Data Analysis and Visualization with R

**Correlation** measures the relationship between two variables and indicates the direction and strength of this relationship. Correlation is represented by a number between 1 and -1. A positive correlation suggests that the variables move in the same direction, while a negative correlation means they move in opposite directions. The closer the correlation coefficient is to 1 or -1, the stronger the correlation, while a value of 0 indicates no correlation.

Explore various correlation test methods like Pearson's, Spearman's, and Kendall's, and learn the differences between them.

Getting Started

File Management

Data Structures

Data Cleaning

Statistical Analysis

Data Transformation

Data Visualization

Conclusion

Evaluation

Netflix Shows

Correlation Tests