Explore GDPR in predictive analytics, including lawful processing, data rights, algorithmic transparency, privacy by design, governance, security and responsible AI practice.
The GDPR & Data Protection in Predictive Analytics course explores how data protection principles relate to predictive systems and data-driven decision-making. It helps learners understand the relationship between predictive analytics, lawful data processing, individual rights and responsible organisational practice.
The course begins by introducing GDPR and data protection in predictive analytics before establishing the foundations of predictive analytics and responsible data use. Learners will consider lawful and ethical processing, data subject rights and the importance of algorithmic transparency.
Further modules examine privacy by design, governance, accountability and impact assessment within predictive environments. The course also addresses risk, security and the future of predictive compliance before considering lawful, ethical and transparent practice in AI applications.
By connecting privacy responsibilities with predictive technologies, this course can support professionals who want to strengthen their understanding of responsible analytics and data protection governance.
8 sections
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40 lectures
Understand the purpose and scope of the course
Explain why GDPR matters in predictive analytics
Define key predictive analytics and data protection terms
Balance innovation with privacy and ethical practice
Navigate the course structure and learning journey
Understand predictive analytics in modern data environments.
Explain the legal context of GDPR and the DPDI Act 2025.
Distinguish profiling from automated decision-making.
Discuss the balance between innovation and privacy.
Identify the ethical imperatives of fairness, accountability, and transparency.
Identify lawful bases for predictive processing.
Explain requirements for special category data and explicit consent.
Conduct and document Legitimate Interest Assessments (LIA).
Apply purpose limitation and data minimisation.
Integrate ethical frameworks in predictive analytics.
Explain key data subject rights, including what individuals are entitled to under data protection regulations.
Describe the importance of transparency in predictive analytics and how it impacts trust and compliance.
Implement procedures for access, rectification, portability, and erasure of personal data to ensure compliance with legal requirements.
Uphold the rights of individuals to object to processing, restrict profiling, and demand explanations regarding automated decisions.
Apply practical measures to achieve algorithmic transparency, making predictive models more understandable and accountable.
Explain the role of privacy throughout the analytics lifecycle, highlighting its importance at each stage.
Distinguish between anonymisation, pseudonymisation, and aggregation, understanding how each method protects data privacy.
Describe advanced privacy techniques such as differential privacy and federated learning, and their applications in analytics.
Identify and apply privacy-enhancing technologies (PETs) to safeguard data during analytics processes.
Document compliance with Data Protection Impact Assessments (DPIAs) and Records of Processing Activities (RoPAs) to meet regulatory requirements.
Foster a privacy-first analytics culture by promoting best practices and awareness among team members.
Understand the role of Data Protection Officers (DPOs).
Learn how to conduct Data Protection Impact Assessments (DPIAs).
Integrate DPIAs into machine learning workflows.
Maintain Records of Processing Activities (RoPA).
Prepare for audits and foster a culture of accountability.
Article 32 Security Obligations
Managing Model Risk and Breaches
Cross-Border Data Transfer Compliance
Navigating Emerging Regulations
Apply Privacy by Design principles throughout AI pipelines to protect user data and ensure compliance with privacy regulations.
Implement automated compliance checks to streamline adherence to legal and ethical standards in AI development and deployment.
Use explainable AI libraries to enhance transparency and make AI decision-making processes more understandable to stakeholders.
Analyse real-world case studies to identify best practices and common challenges in ethical AI implementation.
Anticipate future trends in AI governance to stay ahead of emerging regulations and ethical considerations.
Really useful course — practical, easy to follow and relevant to my work. The section on lawful processing, transparency and risks in data-led predictions stood out for me.
Nice course, easy to dip in and out of. I would have liked more worked impact-assessment examples, but the core material was strong.
This course is suitable for data professionals, privacy and compliance teams, technology staff and others involved in predictive analytics or data-driven systems.
The course introduces the foundations of predictive analytics and data protection, but its coverage of governance, transparency and impact assessment makes some previous awareness of data protection or analytics helpful.
Yes. One module focuses on data subject rights and algorithmic transparency within predictive systems.
Yes. The curriculum includes a dedicated module on privacy by design and data protection in predictive analytics.
No. This course provides general training and educational information only. Organisations and individuals should follow applicable laws, professional guidance, technical standards and internal policies and consult qualified specialists when applying requirements to specific systems.