Attendance rate for semester 1 in SA Government schools by school and year level, collected as part of the annual enrolment data collection in Term 3. Data provided each year from 2018. Important notes: • Attendance rate = (number of days attending school / number of days enrolled) x 100. • Attendance rates are only calculated for full time students who were enrolled or left during Semester 1. • Both whole day and part day absences are counted. • Attendance data is not collected from schools 1717 Watarru Anangu School (non operational), 849 Open Access College, 810 Thebarton Senior College , 583 Marden Senior College, 1012 Northern Adelaide Senior College and 195 Youth Education Centre. • Attendance rates in 2020 are lower than anticipated due to Covid-19 lockdowns.

This dataset shows the attendance rates for all NSW government schools in Semester One by alphabetical order.

Data Notes:

• 2021 data is not comparable to previous years due to the continued effects of the COVID-19 pandemic, changes to calculation rules to align with ACARA’s national standards (version 3) and changes to the way attendance data is transferred into the department’s centralised data warehouse. Please refer to 2021 Semester 1 student attendance factsheet for more information.

• 2020 data is not provided because students were encouraged to learn from home for several weeks in Semester 1. Please refer to the factsheet on The effects of COVID-19 on attendance during Semester 1 2020 for more information.

• In 2018 NSW government schools implemented the national standards for student attendance data reporting. This resulted in a fall in attendance rates for most schools due to the inclusion of part day absences and accounting for student mobility in the calculation. Data from 2018 onwards is not comparable with earlier years.

• Schools for Specific Purposes (SSPs) are only included from 2021. Prior to this SSP attendance data was not collected centrally.

• The attendance rate is defined as the number of actual full-time equivalent student days attended by full-time students in Years 1–10 as a percentage of the total number of possible student-days attended in Semester 1. Figures are aligned with the National Report on Schooling and the My School website.

• Data is suppressed "sp" for schools where student numbers are below the reporting threshold.

• Data is not available "na" for senior secondary schools or other schools where no students were enrolled in Years 1-10.

• Blank cells indicate no students were enrolled at the school that census year or the school was out of scope for attendance reporting.

Data Source:

• Education Statistics & Measurement, Centre for Education Statistics and Evaluation

This data set shows the average attendance rate for students in NSW government schools by Statistical Area 4 (SA4).

Data notes

• 2021 data is not comparable to previous years due to the continued effects of the COVID-19 pandemic, changes to calculation rules to align with ACARA’s national standards (version 3) and changes to the way attendance data is transferred into the department’s centralised data warehouse. Please refer to 2021 Semester 1 student attendance factsheet for more information.

• 2020 data is not provided because students were encouraged to learn from home for several weeks in Semester 1. Please refer to the factsheet on The effects of COVID-19 on attendance during Semester 1 2020 for more information.

• In 2018 NSW government schools implemented the national standards for student attendance data reporting. This resulted in a fall in attendance rates for most schools due to the inclusion of part day absences and accounting for student mobility in the calculation. Data from 2018 onwards is not comparable with earlier years.

• Schools for Specific Purposes (SSPs) are only included from 2021. Prior to this SSP attendance data was not collected centrally.

• The attendance rate is defined as the number of actual full-time equivalent student days attended by full-time students in Years 1–10 as a percentage of the total number of possible student-days attended in Semester 1. Figures are aligned with the National Report on Schooling and the My School website.

• SA4 refers to the ABS Australian Statistical Geography Standard (ASGS) Edition 3 Statistical Area 4 (SA4) – 2021.

• ‘Other Territories’ has been assigned to Norfolk Island Central School, which operated under the responsibility of NSW Department of Education between 2018-2021.

Data source

Semester 1 Return of Absences Collection

Data quality statement

The Attendance Data Quality Statement addresses the quality of the Attendance dataset using the dimensions outlined in the NSW Department of Education's data quality management framework: institutional environment, relevance, timeliness, accuracy, coherence, interpretability and accessibility. It provides an overview of the dataset's quality and highlights any known data quality issues.

This dataset includes the attendance rate for public school students PK-12 by student group and by district during the 2021-2022 school year. Student groups include: Students experiencing homelessness Students with disabilities Students who qualify for free/reduced lunch English learners All high needs students Non-high needs students Students by race/ethnicity (Hispanic/Latino of any race, Black or African American, White, All other races) Attendance rates are provided for each student group by district and for the state. Students who are considered high needs include students who are English language learners, who receive special education, or who qualify for free and reduced lunch. When no attendance data is displayed in a cell, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size. For more information on CSDE data suppression policies, please visit http://edsight.ct.gov/relatedreports/BDCRE%20Data%20Suppression%20Rules.pdf.

📝 Description: This synthetic dataset is designed to help beginners and intermediate learners practice data cleaning and analysis in a realistic setting. It simulates a student tracking system, covering key areas like:

Attendance tracking 📅

Homework completion 📝

Exam performance 🎯

Parent-teacher communication 📢

✅ Why Use This Dataset? While many datasets are pre-cleaned, real-world data is often messy. This dataset includes intentional errors to help you develop essential data cleaning skills before diving into analysis. It’s perfect for building confidence in handling raw data!

🛠️ Cleaning Challenges You’ll Tackle This dataset is packed with real-world issues, including:

Messy data: Names in lowercase, typos in attendance status.

Inconsistent date formats: Mix of MM/DD/YYYY and YYYY-MM-DD.

Incorrect values: Homework completion rates in mixed formats (e.g., 80% and 90).

Missing data: Guardian signatures, teacher comments, and emergency contacts.

Outliers: Exam scores over 100 and negative homework completion rates.

🚀 Your Task: Clean, structure, and analyze this dataset using Python or SQL to uncover meaningful insights!

📌 5. Handle Outliers

Remove exam scores above 100.

Convert homework completion rates to consistent percentages.

📌 6. Generate Insights & Visualizations

What’s the average attendance rate per grade?

Which subjects have the highest performance?

What are the most common topics in parent-teacher communication?

This dataset includes the attendance rate for public school students PK-12 by district during the 2020-2021 school year. Attendance rates are provided for each district for the overall student population and for the high needs student population. Students who are considered high needs include students who are English language learners, who receive special education, or who qualify for free and reduced lunch. When no attendance data is displayed in a cell, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size. For more information on CSDE data suppression policies, please visit http://edsight.ct.gov/relatedreports/BDCRE%20Data%20Suppression%20Rules.pdf.

Attendance rate for semester 1 in SA Government schools by school from 2014. Important notes: • Attendance rate = (number of days attending school / number of days enrolled) x 100. • Attendance rates are only calculated for full time students who were enrolled or left during Semester 1. • Both whole day and part day absences are counted. • Attendance data is not collected from schools 1717 Watarru Anangu School (non operational), 849 Open Access College, 810 Thebarton Senior College , 583 Marden Senior College, 1012 Northern Adelaide Senior College and 195 Youth Education Centre. • To protect the privacy of students, where a school has 5 or less Full Time Equivalent students enrolled, the attendance rate is suppressed for that school. • Attendance rates in 2020 are lower than anticipated due to Covid-19 lockdowns.

Increase the average daily attendance rate in schools from 94.7% in 2014 to 96.7% by 2018.

This dataset contains academic performance scores of students in three subjects — Math, English, and Science — along with their Attendance percentage. It is suitable for analyzing academic trends, performance prediction, and studying the impact of attendance on grades.

Student Performance Prediction Dataset 🎓📊

Overview

This dataset contains information about students' academic performance, study habits, and external factors affecting their final exam scores. It is designed for predictive modeling, data visualization, and educational analytics.

Dataset Purpose

This dataset is useful for:
- Predicting student final exam scores 📈
- Identifying key factors that impact academic performance 🎯
- Exploring feature importance in education-related datasets 📊
- Building machine learning models for regression and classification 🤖

Columns Description

Ideas for Notebooks 📑

1. Regression Analysis – Predict final exam scores using machine learning models (Linear Regression, Random Forest, XGBoost).
2. Feature Importance – Analyze which factors contribute the most to student performance.
3. Exploratory Data Analysis (EDA) – Visualize the impact of study hours, attendance, and other features.
4. Classification – Convert scores into categories (Pass/Fail, A/B/C/D) and build classification models.

License & Usage

This dataset is open for public use. Feel free to use it for learning, research, and model-building! 🚀

This dataset includes the attendance rate for public school students PK-12 by student group and by district during the 2021-2022 school year.

Student groups include:

• Students experiencing homelessness
• Students with disabilities
• Students who qualify for free/reduced lunch
• English learners
• All high needs students
• Non-high needs students
• Students by race/ethnicity (Hispanic/Latino of any race, Black or African American, White, All other races)

Attendance rates are provided for each student group by district and for the state. Students who are considered high needs include students who are English language learners, who receive special education, or who qualify for free and reduced lunch.

When no attendance data is displayed in a cell, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size.

This dataset includes the attendance rate for public school students PK-12 by town during the 2022-2023 school year. Attendance rates are provided for each town for the overall student population and for the high needs student population. Students who are considered high needs include students who are English language learners, who receive special education, or who qualify for free and reduced lunch. When no attendance data is displayed in a cell, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size. For more information on CSDE data suppression policies, please visit http://edsight.ct.gov/relatedreports/BDCRE%20Data%20Suppression%20Rules.pdf.

The attendance rate is defined as the number of actual full-time equivalent student days attended by full-time school students in Years 1–10 as a percentage of the total number of possible student-days attended.

Data Notes:

• Attendance data for NSW government schools only. The attendance rate is calculated as (1 minus absences divided by enrolled days) multiplied by 100.

• This data includes the student attendance rate for semester 1, semester 2 and the full year.

  • Students were learning from home for extended periods during Semester 2 2021 due to COVID-19. As a result, attendance rates for Semester 2 and full year are not reliable and have not been published.

  • 2020 data is not provided because students were encouraged to learn from home for several weeks in Semester 1.

  • For more detail on how attendance data for 2020 and 2021 were affected by COVID-19, please refer to CESE factsheets: ‘Effects of COVID-19 on attendance during Semester 1 2020’ and ‘2021 Semester 1 student attendance'.

• All students in Years 1 to 10 in NSW government schools are regarded as full-time.

• Kindergarten, Year 11, Year 12 students have been excluded in the attendance rates.

• Ungraded (support) student attendance rates are included as a separate row and excluded from Primary and Secondary totals. Ungraded students in NSW government schools are classified as either primary or secondary according to their level of education.

• Distance education and Schools for Special Purposes’ attendance data is not currently collected.

• Bushfires affected many schools' attendance in Term 4 2019 and should be taken into account when comparing Semester 2 data to other years.

• Prior to 2018 absences equalled ‘all full day absences for the period in question’.

• From 2020, students in mainstream support classes are reported by their underlying grade of enrolment. Students in schools for specific purposes (SSPs) are included as 'ungraded'.

• In 2021 attendance figures were calculated differently to align with the third edition of ACARA’s National Standards for Student Attendance Data and Reporting. As a result, data is not directly comparable to previous years.

• The Department implemented an automated attendance feed (AAF) system in Semester 1 2021. The AAF has significantly improved data quality in 2021, which has affected data comparability with previous years.

** Note**

In 2018, NSW government schools implemented the national standards for student attendance data reporting. This resulted in a fall in attendance rates for most schools due to the inclusion of partial absences and accounting for student mobility in the calculation. Data for 2018 is not directly comparable with earlier years.

Source:

• Education Statistics and Measurement. Centre for Education Statistics and Evaluation.

This dataset includes the attendance rate for public school students PK-12 by school during the 2020-2021 school year.

When no attendance data is displayed in a cell, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size. For more information on CSDE data suppression policies, please visit http://edsight.ct.gov/relatedreports/BDCRE%20Data%20Suppression%20Rules.pdf.

Queensland State School data - student attendance rates by region and year level for the last 5 years.

Number and percent of visible minority groups attending school (high school, trades/college or university), for census divisions and municipalities.

The data is collected from all Government schools as part of the administration of student absences. Attendance rate is calculated on the number of actual full-time equivalent student-days attended by students in Years 1 to 10 as a percentage of the total number of possible student-days attended over semester 1. \r
Please note: This definition differs for the data published by ACARA on the 'My School' website where attendance is calculated for Year 1 to Year 10 over the full school year.\r
Please note that due to lockdown periods impacted by COVID-19, the attendance dataset for 2020 might not be comparable to previous year attendance dataset.\r

This dataset shows the attendance rates for all NSW government schools in Semester One by alphabetical order.

Data Notes:

• The attendance rate is defined as the number of actual full-time equivalent student days attended by full-time students in Years 1–10 in Semester One as a percentage of the total number of possible student-days attended in semester one. Figures are aligned with the National Report on Schooling and the MySchool website.

• In NSW government schools, attendance is calculated as (1 minus absences divided by enrolled days) multiplied by 100.

• Data is suppressed "sp" for schools where student numbers are below the reporting threshold.

• Data is not available "na" for senior secondary schools or other schools where no students were enrolled in Years 1-10.

• Blank cells indicate no students were enrolled at the school that census year or the school was out of scope for attendance reporting.

Data Source:

• Statistics Unit. Centre for Education Statistics and Evaluation.

Student Performance & Behavior Dataset

This dataset is real data of 5,000 records collected from a private learning provider. The dataset includes key attributes necessary for exploring patterns, correlations, and insights related to academic performance.

Columns: 01. Student_ID: Unique identifier for each student. 02. First_Name: Student’s first name. 03. Last_Name: Student’s last name. 04. Email: Contact email (can be anonymized). 05. Gender: Male, Female, Other. 06. Age: The age of the student. 07. Department: Student's department (e.g., CS, Engineering, Business). 08. Attendance (%): Attendance percentage (0-100%). 09. Midterm_Score: Midterm exam score (out of 100). 10. Final_Score: Final exam score (out of 100). 11. Assignments_Avg: Average score of all assignments (out of 100). 12. Quizzes_Avg: Average quiz scores (out of 100). 13. Participation_Score: Score based on class participation (0-10). 14. Projects_Score: Project evaluation score (out of 100). 15. Total_Score: Weighted sum of all grades. 16. Grade: Letter grade (A, B, C, D, F). 17. Study_Hours_per_Week: Average study hours per week. 18. Extracurricular_Activities: Whether the student participates in extracurriculars (Yes/No). 19. Internet_Access_at_Home: Does the student have access to the internet at home? (Yes/No). 20. Parent_Education_Level: Highest education level of parents (None, High School, Bachelor's, Master's, PhD). 21. Family_Income_Level: Low, Medium, High. 22. Stress_Level (1-10): Self-reported stress level (1: Low, 10: High). 23. Sleep_Hours_per_Night: Average hours of sleep per night.

The Attendance is not part of the Total_Score or has very minimal weight.

Calculating the weighted sum: Total Score=a⋅Midterm+b⋅Final+c⋅Assignments+d⋅Quizzes+e⋅Participation+f⋅Projects

Dataset contains: - Missing values (nulls): in some records (e.g., Attendance, Assignments, or Parent Education Level). - Bias in some Datae (ex: grading e.g., students with high attendance get slightly better grades). - Imbalanced distributions (e.g., some departments having more students).

Note: - The dataset is real, but I included some bias to create a greater challenge for my students. - Some Columns have been masked as the Data owner requested. "Students_Grading_Dataset_Biased.csv" contains the biased Dataset "Students Performance Dataset" Contains the masked dataset