• Learn from Faculty who are Data Scientists and Alumni of ISB, IIT
  • Learn Python, R programming as part of the Course for Free
  • Soft Skills training included for developing the confidence to face interviews
  • 24/7 Support
  • Internship opportunity for Students

 

Data Science For Iot Data On Cloud

Prerequisite

Computer Analytics Skills

Tools

Python and other related tools

Course Duration

80 Hours

Data Science For Iot Data On Cloud

₹ 58000 ₹26500

What Will I Learn?

Syllabus

 

  • Concepts and Technologies Behind Internet Of Things (IoT)

  • Definitions

  • Myth with IoT

  • Business with IoT

  • Carrier in IoT

  • IoT Applications

  • IoT system overview

  • Node, Gateway, Clouds

  • Why IoT is essential

  • Machine learning

  • Artificial Intelligence

 

  • IoT Network Architecture

  • IoT Device Architecture

  • IoT Device Architecture

  • Publish-Subscribe architecture 

 

  • Sensors – Classification & selection criteria based on the nature, frequency and amplitude of the signal

  • Embedded Development Boards – Arduino, Raspberry Pi, Intel Galileo, ESP8266

 

  • Wired Communication Protocols

  • Wireless Communication Protocols

  • Application Protocols – MQTT, CoAP, HTTP, AMQP 

  • Concept & Architecture of Cloud

  • Public cloud vs Private cloud

  • Different Services in cloud (IAAS / PAAS / SAAS) 

  • Interfacing peripherals & Programming GPIOs – Input/output peripherals, Sensor module

  • Design Considerations – Cost, Performance & Power Consumption tradeoffs

 

 

  • Embedded C

  • Python

  • Arduino

 

  • Setting up board

  • Booting up Raspberry Pi

  • Running python on Raspberry Pi, GPIO programming

  • Interfacing sensors and LED (Input and output devices)

  • Making a few projects

  • Sending data to cloud 2 using Raspberry Pi board

  • Sending data to cloud 3 using Raspberry Pi board

  • Making raspberry Pi web server

  • Making raspberry PI TCP client and server

  • Making raspberry Pi UDP client and server 

 

 

  • A Cloud-based temperature monitoring system using Arduino and Node MC

  • Esp8266 WIFI controlled Home automation

  • Obstacle detection using IR sensor and Arduino

  • Remote controlling with Node MCU

  • Temperature monitoring using a Raspberry Pi as local server

  • Raspberry Pi controlling Esp8266 using MQTT

  • Weather monitoring system using Raspberry Pi

 

  • Existing Product in Market 

  • Overview of Cloud Computing
  • Type of Cloud deployment and service models
  • AWS Global Infrastructure and its benefits
  • Sign-up for AWS free-tier account

 

 

  • Creation of S3 bucket, Version, Security, Replication, transfer Acceleration

  • Types of Storage classes in S3 and costs

  • Life cycle Management in S3

  • Cost optimization for S3

  • CloudFront Create and configure with S3

  • Snowball & Storage Gateway and its types

  • Create an Amazon S3 bucket through Console with a website

  • access & secret access key for an IAM user

  • Create an Amazon S3 bucket through AWS CLI with website

  • Hosting a Static Website on Amazon S3

  • Versioning in AWS S3

  • Replicating data across regions

  • S3 Transfer acceleration

  • Relocate & retrieve data from Glacier through lifecycle policy

  • Upload a file to AWS S3 through a Website

  • Accessing a static website through Cloud Front

 

 

  • User management through Identity Access Management (IAM)

  • Various access policies across AWS Services

  • API keys service access

  • Best practices for IAM Roles

  • Key Management Service

  • Access billing and create alerts on the billing

  • Users creation who can log in to AWS console with Programmatic concept

  • Role creation for an application to access S3

  • Policy Creation for new user to have either admin or limited privileges

  • Credential rotation for IAM users

  • Creation of API keys for accessing AWS Services 

 

  • Start, stop and terminate an EC2 Instance

  • EC2 Pricing

  • Amazon Machine Images (AMI)

  • VPC, Public and Private IP

  • EBS and its types

  • EFS

  • Cost optimization

  • Launch an EC2 instance with a website example

  • Host a website inside EC2

  • Create an Amazon Machine Images (AMI)

  • Create an Elastic IP

  • Attaching an EBS volume externally

  • Create a snapshot

  • Mount EFS volumes

 

 

  • Elastic Load Balancer and its types

  • Comparison of Classic, Network and Application Load Balancer

  • Auto-Scaling components

  • Lifecycle of Auto-Scaling

  • Auto-Scaling policy

  • Working of Route 53 & DNS

  • Various Routing Policies

  • Creation of Classic Load Balancer

  • Creation of Network Load Balancer

  • Work with Application Load Balancer and Auto-Scaling

  • Auto-Scaling and Scaling policy

  • Point a distribution to EC2 box in Route 53

 

 

  • Amazon RDS and its benefits

  • Amazon Aurora

  • Amazon DynamoDB

  • ElastiCache

  • Amazon RedShift

  • AWS Kinesis

  • AWS DMS

  • Storing application data in MySQL DB using RDS

  • Creating Tables, loading sample data and running queries

  • Redis Cache

  • Visualize the web traffic using Kinesis Data Stream

 

 

  • VPC Benefits and Components

  • CIDR Notations

  • Network Access Control List v/s Security Groups

  • Inbound-Outbound, Route Table

  • NAT Network Address Translation

  • VPC peering

  • AWS CloudWatch & CloudTrail

  • Trusted Advisor

  • Create a Non-default VPC and attach it to an EC2 instance

  • Accessing Internet Inside Private Subnet Using NAT Gateway

  • Connect two instances in different VPCs using VPC peering

  • Monitoring an EC2 instance using CloudWatch

  • Enable CloudTrail and Store Logs in S3

  • Explore the Trusted Advisor

 

 

  • AWS Simple Email Service

  • AWS Simple Notification Service

  • AWS Simple Queue Service

  • AWS Simple Work Flow

  • AWS Lambda

  • Send an email through AWS SES

  • Send notification through SNS

  • Send an email through Lambda when the object is added to S3

  • Send notification through Lambda when a message is sent to SQS

 

 

  • AWS CloudFormation & Design patterns

  • AWS OpsWorks for Chef Automate, OpsWorks for Stack, OpsWorks for Puppet Enterprises

  • AWS Elastic Beanstalk

  • Differentiate between CloudFormation, OpsWorks, and Beanstalk

  • Installation of LAMP server in EC2 through CloudFormation

  • AWS OpsWorks Stack

  • Deploy a Web Application with DynamoDB using Beanstalk

 

 

  • Determine how to design HA and fault-tolerant architectures

  • Determine & design de-coupling mechanisms using AWS services

  • Determine how to design a multi-tier architecture solution

  • Choose performant storage

  • Apply caching to improve performance

  • Design solutions for elasticity and scalability

  • Design solution DR and HA for AURORA database

  • Practice Assignment: DR

 

  • Well-Architected Framework

  • Specify Secure Applications and Architectures

  • Design Cost-Optimized Architectures

  • Define Operationally Excellent Architectures

 

 

  • Overview of DevOps - Lifecycle, Stages in DevOps

  • AWS CodeCommit

  • AWS CodePipeline

  • AWS Code Deploy

  • Implement AWS CodeCommit

  • Implement AWS CodePipeline

 

 

  1. Introduction to R and Python

  • Installation

  • Basic Syntaxes

  • Understand Pro’s and Con’s

  • Hands-On

 

  1. Basic Statistics

  • Data types

  • Probability

  • Probability distribution

    • Continuous Probability Distribution

    • Discrete Probability Distribution

  • Sampling Variation

    • Inferential Statistics

    • Sampling Techniques

      • Probability Sampling

      • Non-Probability Sampling

  • Balanced / Unbalanced Data

  • Predictions and Inferencing of Parameters

 

  1. Data Mining

  • Supervised Learning

  • Unsupervised Learning

 

  1. Prediction Analytics

  • Regression Analysis

    1. Linear Regression

    • Scatter plot & Correlation Coefficient

    • OLS

    • LINE assumptions

    • Coefficient of determination

 

    1. Logistic Regression

    • Binomial distribution

    • Link/Probability function

    • Confusion Matrix

    • ROC curve and AUC

 

  1. Data Mining Supervised learning (Classification Techniques)

  • Decision Tree (C5.0)

  • Lazy learner - k-NN classifier

  • Black-Box Techniques: Artificial Neural Network

 

  1. Data Mining Unsupervised learning

  1. Clustering

    • Hierarchical

    • K-means

  1. Dimension Reduction

    • PCA

  1. Association Rules

 

  1. Text Mining

  • Web extraction

  • Bag of Words

  • DTM / TDM

  • Sentiment Analysis using word clouds

 

 

Why Take This Course?

 

Cloud Computing Solution Architect

Cloud Computing is the buzzword and the services here are offered on-demand like Servers, Storage, Networking and Content Delivery, etc. All the 3 types of Cloud services IaaS, PaaS, and SaaS are on the Offer by the Cloud Vendors.

Amazon Web Services is the Leading and Oldest Cloud Service Provider started in 2006. AWS infrastructure is Robust and delivers High Performance for its Clients with its Global Infrastructure consisting of Regions and Availability Zones. High Availability Services offered by AWS assures your application's 100% availability with low cost.

AWS Solutions Architect Certifications based training provides one with a broad understanding of services like EC2, IAM, VPC, S3, EBS, etc for designing and implementing solutions as per the business requirements with AWS Best Practices.

As part of the AWS training participants are taught right from the basics of Cloud and Types of Cloud Services taking the insights of comparison between your own physical data center and cloud infrastructure setup. How to optimize your AWS billing with and reduce the infrastructure cost. One can learn ways to migrate to the cloud with AWS best practices. Learning methodology here would be both theory and practical, making it easy to deploy your cloud services on AWS through Console and CLI.

Data Science

The certification program of Data Science will cover the basics of statistics, Inferential statistics for predictive analytics, Graphical representation of Data to perform Descriptive Analytics, followed by Machine Learning models.

To make students/beginners to the world of analytics understand these business-oriented concepts we culminate the real-time case studies in the learning. Students will gain knowledge on understanding and defining Business problems, Steps in Data collection and Data Cleansing, Feature Engineering, Data Wrangling, Imputation, etc.

The ML for Beginners program is designed to suit students (graduation/post-graduation) or fresh pass outs who eventually want to venture into a career in analytics. Functionals oriented programming languages are used to discuss and implement the concepts of ML. These functional oriented programming languages are fun to learn and are super easy to grasp.

 

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