Bøker av 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 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?

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 thats 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, youll learn how to acquire, clean, analyze, and present data efficiently. Youll 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.Quickly learn basic Python syntax, data types, and language conceptsWork with both machine-readable and human-consumable dataScrape websites and APIs to find a bounty of useful informationClean and format data to eliminate duplicates and errors in your datasetsLearn when to standardize data and when to test and script data cleanupExplore and analyze your datasets with new Python libraries and techniquesUse Python solutions to automate your entire data-wrangling process