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

## Understanding data distribution
**Data distribution** represents how data points are distributed along the axes between the max and min values. 
In other words, the distribution of data represents how many times a data point occurs in the data.
Distribution tells us many things about the data. For example, we know if the data is symmetrical or if it piles up in a specific range by looking at the distribution type.
There are various data distribution types, all having different characteristics. 

# Understanding data distribution
**Data distribution** represents how data points are distributed along the axes between the max and min values. 
In other words, the distribution of data represents how many times a data point occurs in the data.
Distribution tells us many things about the data. For example, we know if the data is symmetrical or if it piles up in a specific range by looking at the distribution type.
There are various data distribution types, all having different characteristics. 

Learn about different types of data distributions and their use cases. 

Data Distribution

Learn about different types of data distributions and their use cases.

Getting Started

File Management

Data Structures

Data Cleaning

Statistical Analysis

Data Transformation

Data Visualization

Conclusion

Evaluation

Netflix Shows

Data Distribution

Understanding data distribution