Healthcare Data Security for Beginners

Healthcare Data Security for Beginners, available at Free, has an average rating of 4, with 57 lectures, based on 45 reviews, and has 1477 subscribers.

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You will learn about Healthcare Data Security Important elements of Health Insurance Portability and Accountability Act Rules (HIPAA) EHR Data Security and Privacy Machine Learning Data Analytics Administrative, physical, and technical safeguards to ensure the confidentiality, integrity, and availability of electronic protected health information Exploratory data analysis Demographic dataset analysis AI and Electronic Healthcare Data (EHR) Importance of Data Privacy and Security From a Patient, Provider and Data User Perspective Storing and Accessing PHI Data Importance of EDA Dataset Schema Analysis Value Distributions Missing Values and Outliers This course is ideal for individuals who are Beginners It is particularly useful for Beginners.

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Summary

Title: Healthcare Data Security for Beginners

Price: Free

Average Rating: 4

Number of Lectures: 57

Number of Published Lectures: 57

Number of Curriculum Items: 57

Number of Published Curriculum Objects: 57

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Healthcare Data Security

Important elements of Health Insurance Portability and Accountability Act Rules (HIPAA)

EHR Data Security and Privacy

Machine Learning

Data Analytics

Administrative, physical, and technical safeguards to ensure the confidentiality, integrity, and availability of electronic protected health information

Exploratory data analysis

Demographic dataset analysis

AI and Electronic Healthcare Data (EHR)

Importance of Data Privacy and Security From a Patient, Provider and Data User Perspective

Storing and Accessing PHI Data

Importance of EDA

Dataset Schema Analysis

Value Distributions

Missing Values and Outliers

Who Should Attend

Beginners

Target Audiences

Beginners

By completing this course, you will learn about:

Data Security and Privacy, including some of the key standards and regulations.

Exploratory data analysis allowing you to gain a deeper understanding of your datasets, including:

Dataset schemas

Value distributions

Missing values

Cardinality of categorical features

Demographic dataset analysis

Data Analytics

Machine Learning

Understand what Machine Learning is and what it offers

Understand the benefits of using the Machine Learning

Understand business use cases and scenarios that can benefit from using the Machine Learning

Understand the different Machine Learning training techniques

Understand the difference between Supervised and Unsupervised training

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Data Security & Healthcare

Lecture 1: EHR Data Security & Analysis Overview

Chapter 3: Importance of Data Privacy and Security in Healthcare

Lecture 1: Data Privacy and Security From a Patient, Provider and Data User Perspective

Lecture 2: Patient Perspective Data Security

Lecture 3: Value of Medical Record Data to Hackers

Lecture 4: Value of Medical Record Data to Hackers – Examples

Lecture 5: Common HIPAA Violations

Lecture 6: Encryption

Chapter 4: Key Healthcare Data Security And Privacy Standards And Terminology

Lecture 1: Key Industry Regulations

Lecture 2: Key Industry Regulations – HIPAA vs. HITECH vs. GDPR vs. DPA

Lecture 3: Shift to Electronic Health Records (EHR)

Lecture 4: What is PHI?

Lecture 5: Covered Entities

Lecture 6: Business Associates (BA)

Lecture 7: Business Associate Agreement/Addendum (BAA)

Chapter 5: Storing and Accessing PHI Data

Lecture 1: Securing PHI Data

Lecture 2: HIPAA Compliant Cloud Practices

Lecture 3: Minimum Necessary Standard

Lecture 4: HIPAAs Two Ways to De-Identify a Dataset

Lecture 5: Importance of EDA

Lecture 6: Reasons EDA is Important

Lecture 7: Value Distributions

Chapter 6: Demographic Analysis

Lecture 1: The Importance of Demographic Analysis In Healthcare

Chapter 7: Data Analytics & Big Data

Lecture 1: Definition of Data Analytics

Lecture 2: Data Classification

Lecture 3: The 3 Vs of Big Data

Lecture 4: Analytics Evolution

Lecture 5: The Analytics Problem Approach

Lecture 6: AWS Big Data and Analytics – Collect

Lecture 7: AWS Big Data and Analytics – Storage

Lecture 8: AWS Big Data and Analytics – Process and Analyze

Chapter 8: Machine Learning

Lecture 1: Key Objective of Machine Learning

Lecture 2: Arthur Samuel Machine Learning Definition

Lecture 3: Tom Mitchell Machine Learning Formal Definition

Lecture 4: Industry Application

Lecture 5: Terminology

Lecture 6: Terminology Examples

Lecture 7: Machine Learning Phases

Lecture 8: Machine Learning Types

Chapter 9: Machine Learning: Supervised Learning

Lecture 1: Supervised Learning Definition

Lecture 2: Supervised Learning Process

Lecture 3: Classification Vs. Regression

Lecture 4: Supervised Learning Algorithms

Lecture 5: Supervised Training Process

Lecture 6: Business Analytics

Lecture 7: Training Summary

Chapter 10: Machine Learning: Unsupervised Learning

Lecture 1: Unsupervised Learning Introduction

Lecture 2: Unsupervised Learning Definition

Lecture 3: Pattern Types

Lecture 4: Clustering

Lecture 5: Anomaly Detection

Lecture 6: Association Discovery

Lecture 7: Clustering Example

Lecture 8: Unsupervised Training Process

Lecture 9: Unsupervised Learning Business Analytics

Lecture 10: Unsupervised Learning Dataset Summary

Lecture 11: Unsupervised Learning Overview

Instructors

Charles Smith
AWS DevSecOps Engineer

Rating Distribution

1 stars: 6 votes

2 stars: 1 votes

3 stars: 3 votes

4 stars: 17 votes

5 stars: 18 votes

Frequently Asked Questions

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