EXECUTIVE CERTIFICATE IN EDUCATIONAL DATA MANAGEMENT & SECURITY

Programme Description

The Executive Certificate in Educational Data Management & Security is designed to equip education professionals with practical knowledge and skills for collecting, organising, storing, protecting, analysing and responsibly using educational data.

The programme focuses on the management and security of data generated by schools and educational institutions, including student records, staff information, assessment results, attendance records, financial information, research data and institutional reports.

Participants will also explore AI and data analytics and learn how technology can support evidence-based decision-making while maintaining data security, privacy and confidentiality. 

Programme Overview

Programme Theme

Managing Educational Data. Protecting Information. Enabling Smarter Decisions.

1. Programme Description

The Executive Certificate in Educational Data Management & Security is designed to equip education professionals with practical knowledge and skills for collecting, organising, storing, protecting, analysing and responsibly using educational data.

The programme focuses on the management and security of data generated by schools and educational institutions, including student records, staff information, assessment results, attendance records, financial information, research data and institutional reports.

Participants will also explore AI and data analytics and learn how technology can support evidence-based decision-making while maintaining data security, privacy and confidentiality.

2. Rationale

Educational institutions generate and manage large volumes of information every day. Poorly managed or inadequately protected data can result in:

• Loss of important records
• Unauthorised access
• Data breaches
• Privacy violations
• Incorrect decision-making
• Loss of stakeholder confidence
• Disruption of school operations

Modern education managers therefore need to understand not only how to manage data, but also how to protect it throughout its lifecycle.

This programme combines educational data management with cybersecurity principles, data analytics and AI.

3. Programme Aim

The programme aims to develop competent and security-conscious education professionals who can manage educational data effectively, protect sensitive information and use data and AI to support institutional decision-making.

4. Programme Objectives

By the end of the programme, participants should be able to:

1. Explain the importance of data management in education.
2. Identify major types of educational data.
3. Apply appropriate data collection and management practices.
4. Classify educational data according to sensitivity and purpose.
5. Apply appropriate storage and access-control practices.
6. Explain data backup and recovery principles.
7. Identify common threats to educational data.
8. Apply basic data-security controls.
9. Use data analytics to support educational decision-making.
10. Explain how AI can support educational data management.
11. Identify privacy and ethical issues associated with educational data.
12. Develop an educational data management and security plan.

5. Learning Outcomes

At the end of the programme, participants will be able to:

• Identify and organise different types of educational data.
• Develop basic educational data-management procedures.
• Classify information according to its sensitivity.
• Apply appropriate access-control principles.
• Develop basic backup and recovery strategies.
• Identify data-security risks.
• Apply practical measures for protecting educational information.
• Analyse educational data to identify patterns and trends.
• Use AI responsibly in educational data analysis.
• Develop a secure data-management framework for a school or educational institution.

6. Target Participants

The programme is suitable for:

• Headteachers
• Assistant headteachers
• School administrators
• Teachers
• ICT coordinators
• Education officers
• School Improvement Support Officers (SISOs)
• EMIS/Data officers
• School proprietors
• Researchers
• Education consultants
• University lecturers
• Postgraduate students
• Data and ICT professionals working in education

7. Entry Requirements

Applicants should normally possess:

• A minimum of SHS certificate or equivalent; and
• Relevant educational, professional or work experience where applicable.

No advanced programming knowledge is required.

8. Programme Structure

MODULE 1

Educational Data Collection & Management

MODULE 2

Data Classification, Storage & Access

MODULE 3

Data Backup, Recovery & Security

MODULE 4

AI, Data Analytics & Secure Educational Data Management

9. DETAILED COURSE OUTLINE

MODULE 1: EDUCATIONAL DATA COLLECTION & MANAGEMENT

Session 1: Understanding Educational Data

Topics

• Meaning of data and information
• Importance of data in education
• Types of educational data
• Student data
• Staff data
• Assessment data
• Attendance data
• Financial and administrative data
• Research data

Practical Activity:
Identify and categorise the major types of data generated by a school.

Session 2: Educational Data Collection

Topics

• Principles of data collection
• Primary and secondary data
• Quantitative and qualitative data
• Questionnaires
• Interviews
• Observation
• Digital data collection
• Data quality and accuracy

Practical Activity:
Design a simple educational data-collection instrument.

Session 3: Data Organisation & Management

Topics

• Data organisation
• Data entry
• Data cleaning
• Data validation
• Data storage
• Data documentation
• Data management procedures
• Data lifecycle

Practical Activity:
Create a basic data-management workflow for a school.

MODULE 2: DATA CLASSIFICATION, STORAGE & ACCESS

Session 4: Educational Data Classification

Topics

• Meaning of data classification
• Public information
• Internal information
• Confidential information
• Sensitive information
• Importance of classification
• Data handling requirements

Practical Activity:
Classify different examples of school information according to their sensitivity.

Session 5: Secure Data Storage

Topics

• Physical and digital storage
• Local storage
• Cloud storage
• Database systems
• Secure file management
• Encryption concepts
• Data retention
• Secure disposal

Practical Activity:
Develop a secure storage plan for student and staff records.

Session 6: Access Control & User Management

Topics

• Meaning of access control
• Authentication
• Authorisation
• User accounts
• Password management
• Multi-factor authentication
• Role-based access
• Least privilege

Practical Activity:
Develop an access-control matrix for a school.

MODULE 3: DATA BACKUP, RECOVERY & SECURITY

Session 7: Data Backup & Recovery

Topics

• Importance of backups
• Types of backup
• Full, incremental and differential backups
• Local and cloud backups
• Backup schedules
• Recovery procedures
• Testing backups

Practical Activity:
Develop a basic backup schedule for a school.

Session 8: Educational Data Security

Topics

• Common data-security threats
• Malware
• Phishing
• Ransomware
• Unauthorised access
• Insider threats
• Lost or stolen devices
• Data breaches

Practical Activity:
Analyse a simulated school data-breach scenario.

Session 9: Data Protection & Privacy

Topics

• Data privacy principles
• Confidentiality
• Student privacy
• Staff privacy
• Consent
• Secure data sharing
• Data breach response
• Ethical responsibilities of data handlers

Practical Activity:
Develop a School Data Protection Checklist.

MODULE 4: AI, DATA ANALYTICS & SECURE EDUCATIONAL DATA MANAGEMENT

Session 10: Educational Data Analytics

Topics

• Meaning of data analytics
• Descriptive analytics
• Educational indicators
• Student performance analysis
• Attendance analysis
• Identifying patterns and trends
• Data-informed decision-making

Practical Activity:
Analyse a simple school dataset and identify key trends.

Session 11: AI & Educational Data Management

Topics

• AI in educational data management
• AI-assisted data analysis
• AI for identifying patterns
• Predictive analytics concepts
• AI and student data
• AI privacy risks
• Bias and responsible AI
• Human oversight

Practical Activity:
Use an AI tool to analyse a sample educational dataset while identifying privacy and accuracy risks.

Session 12: Final Educational Data Security Project

Participants will develop and present an:

Educational Data Management & Security Plan

The project should include:

1. Institutional profile
2. Types of educational data collected
3. Data-collection procedures
4. Data classification
5. Storage arrangements
6. Access-control system
7. Backup strategy
8. Data-security controls
9. Privacy considerations
10. AI/data analytics strategy
11. Incident-response procedures
12. Implementation plan

10. Teaching & Learning Methods

The programme will use:

• Interactive lectures
• Practical demonstrations
• Educational case studies
• Data-management exercises
• Data-analysis activities
• Security scenarios
• Group discussions
• AI demonstrations
• Problem-solving activities
• Institutional projects

Suggested 2-hour session format

20 minutes: Concept introduction
30 minutes: Explanation and examples
30 minutes: Case study/demonstration
30 minutes: Practical activity
10 minutes: Review and questions

This keeps the workload manageable within the 2-hour sessions.

11. Assessment

12. Final Project Options

Participants may choose one of the following:

1. School Data Management & Security Plan
2. Student Records Protection Framework
3. Educational Data Backup & Recovery Plan
4. School Data Classification Framework
5. Educational Data Access-Control System
6. AI-Assisted School Data Analytics Plan
7. School Data Breach Response Plan
8. Secure Digital Records Management System

13. Certification Requirements

Participants must:

• Attend at least 80% of sessions.
• Complete required assignments.
• Participate in practical activities.
• Complete the final project.
• Present the project.
• Meet the required assessment standard.

14. Graduate Profile

A successful graduate should be able to:

Collect, organise, classify, store, protect and analyse educational data while applying appropriate security, privacy and AI practices to support effective educational decision-making.