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:
Cardinality of categorical features
Demographic dataset analysis
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:
Machine Learning Engineers
Big Data Architects
Network Security Engineers
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 schemasValue distributionsMissing valuesCardinality of categorical featuresDemographic dataset analysisData AnalyticsMachine LearningUnderstand what Machine Learning is and what it offersUnderstand the benefits of using the…