About this Dataset

School Database: Comprehensive Educational Data

Overview:

This dataset captures a comprehensive snapshot of an educational institution's ecosystem, encompassing students, teachers, subjects, and academic performance. Designed to enhance data management and facilitate efficient information retrieval, the dataset showcases the intricacies of a well-organized relational database for a school. Each table in the dataset plays a crucial role in capturing and connecting vital elements within the educational landscape.

Tables:

Students
Includes student ID, name, date of birth, gender, and address.
Provides a detailed profile of each student within the institution.

Teachers
Encompasses teacher ID, names, contact details, and associated subjects.
Establishes the connection between teachers and the subjects they instruct.

Subjects
Consists of subject ID and subject names.
Simplifies the representation of academic disciplines.

Marks
Records academic performance, including marks obtained, exam dates, and associated student, teacher, and subject IDs.
Facilitates a comprehensive analysis of student achievements across subjects.

Key Features:

Normalized Structure:
The dataset follows a normalized structure to reduce redundancy and maintain data integrity.

Identity Columns:
Identity columns ensure the uniqueness of primary key values, avoiding duplicate key violations.

Educational Insights:
Offers opportunities for advanced data analysis, including trend analysis, predictive modelling, and decision support.

Scalability and Flexibility:
Designed to accommodate future expansion and evolving data requirements, ensuring scalability and flexibility.

Security Measures:
Identifies opportunities for enhancing data security, including role-based access control and protection of sensitive information.

Potential Use Cases:

Educational Research:
Researchers can analyze trends in academic performance, student-teacher ratios, and subject popularity.

Institutional Decision-Making:
School administrators can derive actionable insights for strategic decision-making and resource allocation.

Educational Technology Development:
Developers can leverage the dataset to create applications that enhance the user experience in educational settings.

How to Use:

Exploratory Data Analysis (EDA):

Dive into the data with exploratory analysis to understand the distribution of students, subjects, and academic performance.

Pattern Recognition:
Identify patterns in student achievements, subject preferences, and teacher-student dynamics.

Predictive Modeling:
Explore predictive modelling to anticipate academic trends and identify potential areas for improvement.

Educational App Development:
Develop educational applications using the dataset to enhance user interfaces and user experiences.
This dataset is a valuable resource for anyone passionate about education, data science, and technology. It provides a holistic view of the educational ecosystem, offering rich insights for exploration and analysis.

Tables

Marks

@kaggle.bernardnm_great_school.marks

6.44 KB
240 rows
6 columns


CREATE TABLE marks (
  "markid" BIGINT,
  "studentid" BIGINT,
  "subjectid" BIGINT,
  "teacherid" BIGINT,
  "markobtained" BIGINT,
  "examdate" TIMESTAMP
);

Students

@kaggle.bernardnm_great_school.students

6.66 KB
20 rows
7 columns


CREATE TABLE students (
  "studentid" BIGINT,
  "firstname" VARCHAR,
  "lastname" VARCHAR,
  "dateofbirth" TIMESTAMP,
  "address" VARCHAR,
  "contactnumber" BIGINT,
  "email" VARCHAR
);

Subjects

@kaggle.bernardnm_great_school.subjects

2.29 KB
10 rows
2 columns


CREATE TABLE subjects (
  "subjectid" BIGINT,
  "subjectname" VARCHAR
);

Teachers

@kaggle.bernardnm_great_school.teachers

6.1 KB
10 rows
7 columns


CREATE TABLE teachers (
  "teacherid" BIGINT,
  "firstname" VARCHAR,
  "lastname" VARCHAR,
  "dateofbirth" TIMESTAMP,
  "address" VARCHAR,
  "contactnumber" BIGINT,
  "email" VARCHAR
);