Learn how to perform data manipulation, analysis, and visualization using R.

Data Science with R

This module covers the techniques for file management, followed by the different data structures used in R. We’ll learn different data cleaning techniques, such as modifying data frames, deleting and adding data, and mutating data. Moreover, we’ll go through various ways to perform statistical analysis on a given dataset, such as by ANOVA, chi-squares, and correlation tests. In the end, we will learn how to transform data using different techniques and how data is visualized with the help of plots like bar plots, pie charts, histograms, etc.

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

Evaluation

Netflix Shows

Conclusion

Quiz on Data Transformation