Data Privacy and Anonymization in Python

DataCamp

(3)

Course Features

Duration

4 hours

Delivery Method

Online

Available on

Limited Access

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Intermediate

Teaching Type

Self Paced

Video Content

4 hours

Course Description

Data privacy is becoming more important than ever. How do you balance privacy with the need to share business insight? This course will show you how to achieve that balance using the same techniques used by Amazon and Google. It includes data generalization and privacy models like differential privacy or k-Anonymity. Learn how to build machine learning models using Python while protecting sensitive information such as income. Let get started!

Course Overview

Virtual Labs

International Faculty

Post Course Interactions

Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Unsupervised Learning in Python

What You Will Learn

Learn to process sensitive information with privacy-preserving techniques

In this course, you'll learn how to do just that, using the same methods as Google and Amazon—including data generalization and privacy models, like k-Anonymity and differential privacy

In addition to touching on topics such as GDPR, you'll also discover how to build and train machine learning models in Python while protecting users’ sensitive information such as employee and income data

Course Content

Expand all sections

Module 1: Introduction to Data Privacy

Module 2: More on Privacy-Preserving Techniques

Module 3: Differential Privacy

Module 4: Anonymizing and Releasing Datasets

Course Instructors

Rebeca Gonzalez

Data Engineer

Rebeca is a data engineer and an entrepreneurial spirit. She has worked in companies like Ayesa and is now co-founder of Alio.li and APTIC, a startup that focuses on helping visually impaired people ...

Course Reviews

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