Introduction

The General Data Protection Regulation (GDPR) is a landmark regulation that governs data privacy and protection for individuals within the European Union (EU) and the European Economic Area (EEA). As data scientists handle vast amounts of personal data, understanding the requirements of GDPR compliance is crucial to ensure that data processing activities respect privacy rights. This course will equip data scientists with the knowledge to navigate GDPR guidelines, implement data protection principles, and safeguard personal data throughout the data science workflow.

Objectives

By the end of this course, participants will:

• Understand the core principles and legal requirements of the GDPR.
• Learn how to assess GDPR compliance for data processing activities.
• Gain practical knowledge of implementing privacy-by-design and data protection-by-default.
• Understand how to handle and protect personal data throughout the data science process.
• Learn how to anonymize and pseudonymize data to comply with GDPR.
• Explore strategies for conducting Data Protection Impact Assessments (DPIAs).
• Understand the consequences of non-compliance and how to mitigate risks.

Who Should Attend?

This course is designed for:

• Data scientists, analysts, and machine learning engineers involved in handling personal data.
• Data privacy officers, compliance officers, and legal professionals.
• Software developers and engineers working with data processing systems.
• Anyone interested in ensuring GDPR compliance in data science workflows.

Day 1: Introduction to GDPR and Data Protection Fundamentals

Morning Session: Overview of GDPR

• The history and scope of GDPR: What it is and why it matters
• Key definitions in GDPR: Personal data, sensitive data, data subject, data controller, and processor
• Rights of the data subject: Right to access, right to erasure, right to rectification, etc.
• The role of data controllers and processors in compliance
• Hands-on: Reviewing the GDPR structure and identifying key provisions relevant to data scientists

Afternoon Session: Data Protection Principles

• The 7 key principles of data processing under GDPR
  • Lawfulness, fairness, and transparency
  • Purpose limitation
  • Data minimization
  • Accuracy
  • Storage limitation
  • Integrity and confidentiality
  • Accountability
• How these principles apply to data science practices
• Case study analysis: Real-world examples of GDPR violations and their impact
• Hands-on: Identifying how to implement the principles in a data science workflow

Day 2: Understanding Personal Data and Data Processing Activities

Morning Session: Categories of Personal Data and Special Considerations

• Types of personal data: Basic personal data, sensitive data (e.g., health data, biometric data), and pseudonymized data
• GDPR’s stance on processing special categories of data and the conditions for processing them
• How to classify and categorize data for GDPR compliance
• Hands-on: Identifying personal data and special categories within datasets

Afternoon Session: Data Processing Activities and Consent

• Defining and documenting data processing activities
• Legal bases for processing personal data: Consent, contract performance, legal obligation, legitimate interest, etc.
• How to obtain, record, and manage consent under GDPR
• Requirements for processing data with consent: Clarity, explicitness, and revocation
• Hands-on: Implementing consent management and assessing processing activities for legal basis compliance

Day 3: Privacy by Design and Data Security

Morning Session: Privacy by Design and Privacy by Default

• Understanding the concept of privacy by design and its importance for GDPR compliance
• How to integrate data protection principles into every phase of the data science lifecycle
• Data protection by default: Ensuring that privacy settings are at their highest level by default
• Practical examples: How to design data pipelines with privacy in mind
• Hands-on: Implementing privacy-by-design strategies in a data science project

Afternoon Session: Data Security Measures and Breach Notification

• Ensuring data security: Encryption, access control, anonymization, and pseudonymization
• How to implement data security in machine learning and big data environments
• GDPR’s breach notification requirements: When and how to report data breaches
• The role of data processors in ensuring data security
• Hands-on: Identifying and mitigating security risks in a data science project

Day 4: Anonymization, Pseudonymization, and Data Protection Impact Assessments (DPIAs)

Morning Session: Anonymization and Pseudonymization

• The difference between anonymization and pseudonymization under GDPR
• Techniques for anonymizing and pseudonymizing personal data
• When to use anonymized vs pseudonymized data in data science projects
• The risks and benefits of anonymization and pseudonymization
• Hands-on: Implementing anonymization and pseudonymization techniques on datasets

Afternoon Session: Data Protection Impact Assessments (DPIAs)

• What is a Data Protection Impact Assessment (DPIA) and when it is required
• How to conduct a DPIA: Identifying, assessing, and mitigating privacy risks
• The role of DPIAs in ensuring GDPR compliance for data science activities
• Hands-on: Conducting a DPIA for a data science project involving personal data

Day 5: Managing Data Subject Rights and GDPR Compliance Tools

Morning Session: Data Subject Rights and How to Handle Requests

• Managing data subject rights under GDPR: Right to access, right to rectification, right to erasure, etc.
• Responding to data subject requests: Timeframes and procedures for compliance
• How to implement systems for processing data subject requests in data science workflows
• Hands-on: Simulating a data subject access request and responding in compliance with GDPR

Afternoon Session: Tools and Strategies for Ongoing GDPR Compliance

• Implementing automated compliance checks and data governance frameworks
• Tools and technologies for GDPR compliance: Data discovery, classification, and management
• Continuous monitoring and auditing of data processing activities
• Best practices for maintaining GDPR compliance over time
• Hands-on: Implementing a data governance tool for ongoing GDPR compliance

Materials and Tools:

• Required tools: Jupyter Notebooks (for demonstrating GDPR workflows), Python libraries (e.g., Pandas for data processing and anonymization), and compliance frameworks
• Access to GDPR-compliant datasets for hands-on exercises
• Case studies and real-world examples of GDPR compliance challenges

Conclusion and Final Assessment

• Recap of the GDPR principles, obligations, and compliance strategies
• Group discussions on GDPR challenges in data science and potential solutions
• Final assessment: Participants will present how they would ensure GDPR compliance in a given data science project
• Certification of completion awarded to participants who successfully complete the course