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

Which encoding technique is the best choice to encode military titles?


Which encoding technique is the best choice to encode military titles?

**(Select all that apply.)** Which option(s) help detect outliers in a dataset? 



(Select all that apply.) Which option(s) help detect outliers in a dataset?

The process of combining data from multiple sources into a single, unified view

The process of assigning one single value to multiple locations

The process of selecting only certain rows or columns of a dataset based on certain criteria

The process of reshaping a dataset by transforming one or more of its columns into rows

To involve missing values in calculations

Why is the missing data imputed in data analysis? 

Why is the missing data imputed in data analysis?

**(Select all that apply.)** Which methods are used to deal with missing values in data?



(Select all that apply.) Which methods are used to deal with missing values in data?

Quiz - Data Transformation

Quiz - Data Transformation

Test your understanding of data transformation.

Getting Started

File Management

Data Structures

Data Cleaning

Statistical Analysis

Data Transformation

Data Visualization

Conclusion

Evaluation

Netflix Shows

Quiz on Data Transformation