Overview
Course Outline
· Introduction to Amazon Kinesis Streams
In this course, we cover how Amazon Kinesis Streams is used to collect, process and analyze real-time streaming data to create valuable insights. An overview of the components of this service and a brief demonstration are also covered in this course.
· Introduction to Amazon Kinesis Analytics
This is an introductory course on Amazon Kinesis Analytics, which helps you query streaming data or build entire streaming applications using SQL. In this course, we discuss how the service collects, processes and analyzes streaming data in real-time. We also discuss how to use and monitor Amazon Kinesis Analytics and explore use cases.
· Getting Started with Amazon EMR
Amazon EMR is a service for processing vast amounts of data using open-source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi, and Presto. You can use Amazon EMR to set up, operate, and scale your big data environments and automate time-consuming tasks like provisioning capacity and tuning clusters. In this course, you will learn the benefits, typical use cases, and technical concepts of Amazon EMR.
· Getting Started with AWS Glue
AWS Glue is a serverless data integration service that you can use to discover, prepare, and combine data for analytics, machine learning (ML), and application development. In this course, you will learn the benefits, typical use cases, and technical concepts of AWS Glue.
· Introduction to Amazon Athena
This course introduces the Amazon Athena service along with an overview of its operating environment. The basic steps in implementing Amazon Athena are also covered. Using the AWS Management Console, a brief demonstration of creating a database to run SQL queries for validation is performed.
· Introduction to Amazon Quicksight
This is an introductory video on Amazon QuickSight – the cloud-powered business analytics service that makes it easy to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. In this course, we will discuss the benefits of using Amazon QuickSight and how the service works.
· Visualizing with QuickSight
In this course, you will be introduced to the technical side of business intelligence (BI) and data visualization with Amazon Web Services (AWS). You will focus on using Amazon QuickSight to build and share interactive dashboards and analyses.
· Introduction to AWS IoT Analytics
This course is an introduction to AWS IoT Analytics, a fully-managed service which allows you to run sophisticated analytics on massive volumes of IoT data. In this course, we look at the key components of AWS IoT analytics, an overview of the deployment architecture, and some use cases. We also have a demo for you to see AWS IoT Analytics in action.
· Data Analytics Fundamentals
Introduction to data analysis solutions
– Data analytics and data analysis concepts
– Introduction to the challenges of data analytics
Volume – data storage
– Introduction to Amazon S3
– Introduction to data lakes
– Introduction to data storage methods
Velocity – data processing
– Introduction to data processing methods
– Introduction to batch data processing
– Introduction to stream data processing
Variety – data structure and types
– Introduction to source data storage
– Introduction to structured data stores
– Introduction to semistructured and unstructured data stores
Veracity – cleansing and transformation
– Understanding data integrity
– Understanding database consistency
– Introduction to the ETL process
Value – reporting and business intelligence
– Introduction to analyzing data
– Introduction to visualizing data
· Machine Learning for Business Challenges
Machine learning (ML) can help you solve business problems in ways that weren’t possible before.
· Machine Learning Terminology and Process
This course introduces you to basic machine learning concepts and the machine process the data goes through. We explore each step in the machine learning process in detail and explain some of the common terms and techniques that occur during a phase of a ML project.
· Exploring the Machine Learning Toolset
No matter what your background or experience, you can use machine learning. In this course, we’ll show you some of the AWS machine learning services you can use to build models and add intelligence to applications.
· The Elements of Data Science
Learn to build and continuously improve machine learning models with Data Scientist Harsha Viswanath, who will cover problem formulation, exploratory data analysis, feature engineering, model training, tuning and debugging, as well as model evaluation and productionizing.
· Math for Machine Learning
To understand modern machine learning, you also need to understand vectors and matrices, linear algebra, probability theorems, univariate calculus, and multivariate calculus.
· Developing Machine Learning Applications
In this curriculum, we’ll explore Amazon’s fully managed ML platform, Amazon SageMaker. Specifically, we’ll discuss how to train and tune models, how certain algorithms are built in, how you can bring your own algorithm, and how to build for particular use cases like recommender systems or anomaly detection.
· Amazon SageMaker: Build an Object Detection Model
Downloading data
Running a labeling job
Training a model
Hyperparameters/automated model tuning
Examining hyperparameter optimization results
Assignments for assessment
One Project
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