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 value is the highest in a right-skewed dataset?

Which value is the highest in a right-skewed dataset?

Which distribution has the same probability of occurrence for all observations?

Which distribution has the same probability of occurrence for all observations?

The distribution of the averages of a large number of samples from any type of data approximates a normal distribution.

In a normal distribution, the data within the 2 standard deviation range from the mean makes up 95% of the total.

The mean is bigger than the median in left-skewed data.

Variance equals the square of the standard deviation.

According to the central limit theorem, which one of the following is correct? 

According to the central limit theorem, which one of the following is correct?

The difference between the mean and the median

The difference between the mode and the median

The difference between the maximum and the minimum values

The difference between the 25th percentile and the 75th percentile

A statistical measure to compare the variability between two sets of data used in hypothesis testing

A range of possible values that the true population parameter is likely to fall within, with a certain level of likelihood

A measure of the number of standard deviations a data point is from the mean of a dataset

A statistical measure representing the extreme probability of observing a test statistic, assuming that the null hypothesis is true.

To measure the 90% confidence interval of salaries

To test the independence of two categorical variables

To compare the means of two or more independent groups

To define the maximum and the minimum values in data

In which situation do we use the ANOVA?

What is *not* a method to test the normality of data?

What is not a method to test the normality of data?

Level of significance ($\alpha$) < F-value

p-value < Level of significance ($\alpha$)

F-value < Level of significance ($\alpha$)

Level of significance ($\alpha$) < p-value

Which condition indicates that the null hypothesis is rejected?

Which condition indicates that the null hypothesis is rejected?

A zoologist wants to ensure that a newly encountered ant breed is not a known breed. The length of the antenna is an important factor in distinguishing the breeds from each other. We have data on antenna length for samples from various breeds. Which statistical test should we use to ensure that the breed is unique?

A zoologist wants to ensure that a newly encountered ant breed is not a known breed. The length of the antenna is an important factor in distinguishing the breeds from each other. We have data on antenna length for samples from various breeds. Which statistical test should we use to ensure that the breed is unique?

Getting Started

File Management

Data Structures

Data Cleaning

Statistical Analysis

Data Transformation

Data Visualization

Evaluation

Netflix Shows

Conclusion

Quiz on Statistical Analysis