Katharine Jarmul

Between major privacy regulations like the GDPR and CCPA and expensive and notorious data breaches, there has never been so much pressure to ensure data privacy. Unfortunately, integrating privacy into data systems is still complicated. This essential guide will give you a fundamental understanding of modern privacy building blocks, like differential privacy, federated learning, and encrypted computation. Based on hard-won lessons, this book provides solid advice and best practices for… Mehr anzeigen

Between major privacy regulations like the GDPR and CCPA and expensive and notorious data breaches, there has never been so much pressure to ensure data privacy. Unfortunately, integrating privacy into data systems is still complicated. This essential guide will give you a fundamental understanding of modern privacy building blocks, like differential privacy, federated learning, and encrypted computation. Based on hard-won lessons, this book provides solid advice and best practices for integrating breakthrough privacy-enhancing technologies into production systems.

Practical Data Privacy answers important questions such as:

What do privacy regulations like GDPR and CCPA mean for my data workflows and data science use cases?
What does "anonymized data" really mean? How do I actually anonymize data?
How does federated learning and analysis work?
Homomorphic encryption sounds great, but is it ready for use?
How do I compare and choose the best privacy-preserving technologies and methods? Are there open-source libraries that can help?
How do I ensure that my data science projects are secure by default and private by design?
How do I work with governance and infosec teams to implement internal policies appropriately? Weniger anzeigen

An introduction to building automated workflows / data pipelines and DAGs using Python tools and libraries.

An Introduction to Data Wrangling using Pandas

How do you take your data analysis skills beyond Excel to the next level? By learning just enough Python to get stuff done. This hands-on guide shows non-programmers like you how to process information that’s initially too messy or difficult to access. You don't need to know a thing about the Python programming language to get started.

Through various step-by-step exercises, you’ll learn how to acquire, clean, analyze, and present data efficiently. You’ll also discover how to automate… Mehr anzeigen

How do you take your data analysis skills beyond Excel to the next level? By learning just enough Python to get stuff done. This hands-on guide shows non-programmers like you how to process information that’s initially too messy or difficult to access. You don't need to know a thing about the Python programming language to get started.

Through various step-by-step exercises, you’ll learn how to acquire, clean, analyze, and present data efficiently. You’ll also discover how to automate your data process, schedule file- editing and clean-up tasks, process larger datasets, and create compelling stories with data you obtain.

Ditch your Excel formulas and get started with Python today! Weniger anzeigen