Gain insights into data manipulation, analysis, and visualization with R. Learn statistical concepts, create insightful visualizations, and empower decision-making to excel in data-centric roles.

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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.

It's necessary to keep in mind that just because two variables correlate doesn't mean that one causes the other. There may be other factors at play that are causing both variables to behave in a certain way.
For example, there's a correlation between the number of shark attacks and ice cream sales, both of which rise during summer. But does that mean that ice cream sales are causing the shark attacks? No, the reason is the increase in temperature during the summer.

There are various statistical methods to calculate the correlation coefficient. The most common ones are Pearson's, Spearman's, and Kendall's. We pick the correlation method according to the data characteristics and problem at hand.

# Pearson's correlation

We use **Pearson's correlation** to measure the linear relationship between two continuous variables. It is appropriate to use this method when both variables are normally distributed, and the relationship between variables is linear. 

The `cor()` function helps calculate the correlation coefficient. It takes two variables and returns the correlation estimate. The structure is as follows:

```R
cor(<variable1>,<variable2>)
```
Let's continue with an example.




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

Uber Data Analysis Using the R Language

Conclusion

Evaluation

Netflix Shows

Correlation Tests