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Data Privacy and Anonymization in R

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$12

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Course Features

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Duration

4 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Mobile, Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

4 hours

Course Description

With big data and social media, data privacy is becoming a major concern. Google, Apple, US Census Bureau and other organizations have started to advocate better privacy techniques, particularly differential privacy. This is a mathematical condition that quantifies privacy risk. This course will teach you how to code basic data privacy methods as well as a differentially-private algorithm that is based on different differentially private properties. You can use these tools to create basic synthetic data sets (fake data), with the differential privacy guarantee, which allows public data release.

Course Overview

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Virtual Labs

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International Faculty

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Post Course Interactions

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Hands-On Training,Instructor-Moderated Discussions

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Case Studies, Captstone Projects

Skills You Will Gain

Prerequisites/Requirements

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Intermediate R

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Foundations of Probability in R

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Introduction to the Tidyverse

What You Will Learn

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In this course, you will learn to code basic data privacy methods and a differentially private algorithm based on various differentially private properties

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With these tools in hand, you will learn how to generate a basic synthetic (fake) data set with the differential privacy guarantee for public data release

Course Instructors

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Claire Bowen

Postdoctoral Researcher at the Los Alamos National Laboratory

Claire McKay Bowen is a Postdoctoral Researcher in the Statistical Science Group at the Los Alamos National Laboratory. She conducts research in uncertainty quantification with physics-informed Bayes...

Course Reviews

Average Rating Based on 3 reviews

5.0

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Course Cover

$12

$6

49% OFF

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