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Student Performance Analysis Project for Beginners Using Data Science

Student Performance Analysis Project for Beginners Using Data Science

The Student Performance Analysis project is a beginner-friendly data analytics project that explores how factors like attendance and study hours impact exam scores. Using Python, pandas, Matplotlib, and Seaborn in Google Colab, it demonstrates how raw student data is cleaned, analyzed, and visualized to generate meaningful academic insights.

6 Modules

30 Lessons

English

0.5 Hr

Reading Plan

MODULE 1

Introduction

4 mins

Introduction

1 min

What Is Data Science?

1 min

What Is Student Performance Analysis?

1 min

Aim of the Project

1 min

MODULE 2

Pre-requisites and Tech Stack Used

4 mins

Pre-requisites & Tech Stack Used

1 min

Basic Requirements

1 min

Tech Stack Used

1 min

Input and Output

1 min

MODULE 3

Necessary Concepts

6 mins

Necessary Concepts

1 min

Data Cleaning

1 min

Handling Missing Values

1 min

Feature Engineering

1 min

Data Aggregation and Grouping

1 min

Exploratory Data Analysis (EDA) and Charts Used in the Analysis

1 min

MODULE 4

Step-by-Step Implementation

6 mins

Step-by-Step Implementation

1 min

Dataset Download and Environment Setup

1 min

Loading the Dataset

1 min

Understanding the Dataset

1 min

Data Cleaning and Handling Missing Values

1 min

Exploratory Data Analysis and Visualization

1 min

MODULE 5

Data Analysis and Insights

5 mins

Data Analysis and Insights

1 min

Performance Trends With Attendance

1 min

Performance Comparison Between Previous Scores and Exam Scores

1 min

Performance Change Analysis (Previous Scores vs Exam Scores)

1 min

Correlation Analysis Between Academic Factors and Exam Score

1 min

MODULE 6

Conclusion

5 mins

Conclusion

1 min

Learning Outcome

1 min

Live Output and Analysis Overview

1 min

MCQs

1 min

Resources

1 min

Contributors

Jebasta

Student Performance Analysis Project for Beginners Using Data Science

Learn how to analyze real-world student performance data using Python to uncover attendance trends, study patterns, and factors influencing exam scores. This beginner-friendly handbook guides you through data cleaning, feature engineering, exploratory analysis, and visualization using Pandas, Matplotlib, and Seaborn in Google Colab.

Student Performance Analysis

This handbook helps learners gain hands-on experience in data analysis by working with a real-world student performance dataset. It explains how to clean, transform, and visualize academic data to uncover attendance trends, study patterns, performance changes, and key factors influencing exam scores, all in a clear and beginner-friendly way using Python, Pandas, Matplotlib, and Seaborn.

Student Performance Analysis for Beginners

This project is ideal for beginners who want to get started with data analysis. It’s perfect for students, freshers, and anyone with basic Python knowledge who wants to understand how real-world student performance data can be cleaned, analyzed, and visualized through practical, hands-on implementation.

Prerequisites

This course is suitable for:

Basic knowledge of Python programming
Basic understanding of data analysis concepts (columns, rows, missing values)
A Google account to access Google Colab
Access to the sales dataset in CSV format
Internet connection to upload the dataset and run the project in Colab

Run & Test your Code with our very own IDE that supports 16 languages

Open IDE

6 Modules

30 Lessons

English

0.5 Hr

Contributors

Jebasta

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