5 Courses – Master AWS, Analytics, Machine Learning, Bigdata

Course Curriculum

Chapter 1: AWS Cloud Services

Lecture 1: Embrace Cloud computing

Lecture 2: AWS Products and Solutions

Lecture 3: AWS-Pricing – Simple Monthly calculator – TCO Calculator

Lecture 4: AWS Compute products and services

Lecture 5: Amazon Elastic Compute Cloud EC2

Lecture 6: Amazon EC2 – Dashboard

Lecture 7: Amazon EC2 – Launch Linux VM

Lecture 8: Amazon EC2 – Access Linux VM

Lecture 9: Amazon EC2 – Launch and access Windows server VM

Lecture 10: Amazon EC2 – Launch WordPress website

Lecture 11: AWS Elastic Beanstalk

Lecture 12: Amazon EC2 Autoscaling

Lecture 13: Elastic Load Balancing (ELB)

Lecture 14: Amazon Lightsail

Lecture 15: AWS Lamda

Lecture 16: AWS ECS using Fargate

Lecture 17: Storage – S3, EBS, EFS, Glacier, Snow Family, Gateway

Lecture 18: Storage S3 – create bucket, store retrieve files

Lecture 19: Access S3 through AWS – CLI with IAM user credentials

Lecture 20: Route 53, Domain name, DNS

Lecture 21: AWS Databases

Lecture 22: AWS – RDS MySQL – Create, connect and operate

Lecture 23: Amazon Dynamo DB

Lecture 24: Migration Hub

Lecture 25: Security, Identity, & Compliance

Lecture 26: AWS Step Functions

Lecture 27: Amazon CloudWatch monitoring and management service

Chapter 2: AWS Data Analytics

Lecture 1: AWS Analytics and Data Lakes

Lecture 2: Amazon Athena – Interactive query service

Lecture 3: Amazon CloudSearch – Managed search service

Lecture 4: Amazon Elasticsearch Service

Lecture 5: Amazon Kinesis – Data Streams – Visualizing Web Traffic Using Amazon Kinesis Dat

Lecture 6: Amazon Kinesis – Data Streams using AWS CLI

Lecture 7: Amazon Redshift – Data warehousing

Lecture 8: Amazon QuickSight – Business Analytics Intelligence Service

Lecture 9: Amazon Data Pipeline – Automate data movement

Lecture 10: AWS Glue – Managed ETL Service

Chapter 3: Bigdata and Hadoop framework

Lecture 1: 1.1 Big data introduction

Lecture 2: 1.2 Big data history

Lecture 3: 1.3 Big data technologies

Lecture 4: 1.4 Big data characteristics

Lecture 5: 1.5 Big data Applications

Lecture 6: 1.6 Data Lake

Lecture 7: 1.7 Data science and Data scientist

Lecture 8: 2.1 – Hadoop introduction

Lecture 9: 2.2 – HDFS-Overview

Lecture 10: 2.3 – Hadoop Architecture

Lecture 11: 2.3a – Hadoop Architecture – assumptions and goals

Lecture 12: 2.4 – Demo-Hadoop install – sw download verify integrity

Lecture 13: 2.5 – Demo-Hadoop install – Java ssh configure

Lecture 14: 2.6 Demo hadoop access by browser

Chapter 4: Machine Learning

Lecture 1: 3.1 Machine learning introduction

Lecture 2: 3.2 Machine learning algorithms

Lecture 3: 3.3 Machine learning softwares

Chapter 5: AWS Machine Learning

Lecture 1: 5.1 AWS and Machine learning

Lecture 2: 5.2 Bigdata and AWS

Lecture 3: 5.3 Hadoop on Amazon Elastic Map Reduce – EMR

Lecture 4: 5.4 Amazon Elastic MapReduce -EMR

Lecture 5: 5.5 Amazon EMR Architecutre

Lecture 6: 5.6 Amazon EMR benefits

Lecture 7: 5.7 Launch Amazon EMR cluster

Lecture 8: 5.8 TensorFlow – Open source Machine Learning framework

Lecture 9: 5.9 Amazon SageMaker – TensorFlow Part 1

Lecture 10: 5.9 Amazon SageMaker – TensorFlow Part 2

Lecture 11: 5.10 AWS Deep Learning AMIs

Lecture 12: 5.11 AWS Translate – Natual language translation

Lecture 13: 5.13 Amazon Polly – turn text to speech

Lecture 14: 5.14 Apache MXNet – Deep learning framework