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


**Inferential statistics** is a branch of statistics that makes inferences or predictions about a population based on a sample. In inferential statistics, we use models, formulas, and other varied methods to make estimates, predictions, and generalizations about a population. Using these methods, we can estimate population parameters, find confidence intervals, conduct hypothesis tests, and compare samples.

## Population and sample

**Population** refers to all the individuals or elements we are interested in for a particular statistical study. All people living in a country are an example of a population.



**Inferential statistics** is a branch of statistics that makes inferences or predictions about a population based on a sample. In inferential statistics, we use models, formulas, and other varied methods to make estimates, predictions, and generalizations about a population. Using these methods, we can estimate population parameters, find confidence intervals, conduct hypothesis tests, and compare samples.

# Population and sample

**Population** refers to all the individuals or elements we are interested in for a particular statistical study. All people living in a country are an example of a population.


Get introduced to sampling methods and terms.

Getting Started

File Management

Data Structures

Data Cleaning

Statistical Analysis

Data Transformation

Data Visualization

Conclusion

Evaluation

Netflix Shows

Inferential Statistics

Population and sample