AWS Healthcare Data Analytics For Beginners

Introduction to AWS Healthcare Data Analytics

By completing this course, you will learn about:

What you’ll 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.
  • Data Analytics tools provided by AWS.
  • Elastic Map Reduce (EMR).
  • Data Pipeline.
  • Elasticsearch.
  • Kinesis.
  • Amazon Machine Learning.
  • QuickSight.
  • Data Science and Analytics concepts.
  • Steps of Big Data and Data Processing.
  • Databases.

Course Content

  • Introduction –> 1 lecture • 1min.
  • Data Security & Healthcare –> 1 lecture • 1min.
  • Importance of Data Privacy and Security in Healthcare –> 6 lectures • 4min.
  • Key Healthcare Data Security And Privacy Standards And Terminology –> 7 lectures • 4min.
  • Storing and Accessing PHI Data –> 7 lectures • 5min.
  • Demographic Analysis –> 1 lecture • 1min.
  • Data Analytics & Big Data –> 8 lectures • 11min.
  • Machine Learning –> 8 lectures • 6min.
  • Machine Learning: Supervised Learning –> 7 lectures • 5min.
  • Machine Learning: Unsupervised Learning –> 10 lectures • 5min.

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  • Basic Computer Knowledge.

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
    • Gain full visibility into your AWS application code performance with end-to-end tracing, profiling, and App Analytics
    • Identify critical issues quickly with real-time service maps and alerts on code-level + service-level performance issues
    • Test hypotheses in seconds by overlaying events onto time-synchronized graphs


    We will start with an overview of Data Science and Analytics concepts to give beginners the context they need to be successful in the course. The second part of the course will focus on the AWS offering for Analytics, this means, how AWS structures its portfolio in the different processes and steps of big data and data processing.

    • In this course, we will also explore the Analytics tools provided by AWS, including Elastic Map Reduce (EMR), Data Pipeline, Elasticsearch, Kinesis, Amazon Machine Learning.

Who This Course Is For:

  • Data Scientists
  • Data Engineers
  • Machine Learning Engineers
  • Big Data Architects
  • Healthcare Professionals
  • Solutions Architects
  • Cloud Engineers
  • DevOps Engineers
  • Cybersecurity Analysts
  • Network Security Engineers
  • System Administrators
  • Programmers