Project Name : PROJECT_EXAM_SCORE

This project gives us the understanding of the exam scores of different students in different subjects. We perform data understanding, data manipulation and data visualization in this project.

Introduction:

Here in this project I use R programming language to undertsnad, work and analysis a data set called Exam_Score data set, which is created by me. This data set contains different scores of different subjects of different students.

Into the project Explanation:

1. Installing and loading Packages:

Here I use the main data handling package in R known as tidyverse package. Under this packages I Use some libraries:

• tidyverse library
• dplyr library
• ggplot2 library

2. Reading the file:

In step two I read the contains on the .csv file by using read.csv() function which comes under the readr library of tidyverse package.

3. Data Understanding:

This is the third step of thi process. This includes,differnet works that I do to understand the data . They include:

• Previewing the data set
• Getting structure of the data set
• Getting summary statistics of the data
• Getting first few rows of the data
• Getting number of rows and columns of the data set.

4. Data manipulation:

This step is alos known as the data preparation step. In this step we address missing values, correct errors of the data set, transfor the data into a useable format so that the data frame can be analysed further. This includes different functions such as:

• select()- to select variables
• mutate()- to create new variables based on values
• filter()- to filter rows using a specific condition
• group_by()- to organize all types of objects
• summarize()- to calculate Mathematical values
• There are other functions too

5. Data analysis by Visualization:

In this step I use the ggplot() function which comes under the ggplot2 library that comes under the tidyverse package. This library help to make publixcation level graphical input. Some plotting objects are:

• Dot plot or scatter plot
• Bar chart
• Line chart
• Histogram
• Pie chart
• Density Chart
• Boxplot
• There are other plot objects too.

Conclusion part:

• Average Marks Score in Maths is 82.12
• Minimum munber scored in Maths is 45
• Maximum Number Scored in Maths is 99.
• There is only a person who get a Remark 'Good' after getting 90+ in maths
• Male have higher variability in Maths and Science Scores
• Male have higher variability in English and Bengali Scores
• Male have higher variability in Science and Geography Scores.
• Our study shows that male are grater than female,there can be some reasons behind this.
• Number of females are considerably less than the number of males
• There may be a bias in the data
• Age of students shows a great interpretation of our result.
• For 21 years old persons, there are more variability in the Marks Scored in exam