Enterprise IoT

  • Overview
  • Schedule Classes
  • What you'll learn
  • Curriculum
  • Feature
  • FAQs
$999.00
Enroll Now
overviewbg

Overview

With increasing connectivity across devices, Internet of Things has gained huge popularity around the globe today. Industries are spending massively on exploring avenues with IoT and coming up with countless innovations and developments. The Enterprise IoT training course offered by Cognixia focuses on all aspects related to IoT – Architecture, Data Analytics, Machine Learning with Python, the IoT Cloud platform and IoT Security. The training looks at IoT from an architect’s point of view. The course equips participants with the essential skills and expertise to help them develop integrated solutions that would meet the business requirements.

Schedule Classes


Looking for more sessions of this class?

Talk to us

What you'll learn

  • Gaining in-depth knowledge on IoT Systems
  • Building sound understanding of core concepts, background technologies and sub-domains of IoT.
  • Familiarizing IoT-related components, sensors, SBCs, etc.
  • Understanding the IoT architecture and architecting end-to-end systems
  • Learning IoT supporting technologies
  • Integrating IoT with enterprise and user applications (Mobile Phone/SCADA/Enterprise Apps)
  • Understanding the business needs and translating them into technical strategy to implement IoT architecture and solutions models
  • Expert-level knowledge of IoT technology, tools and trends.

Prerequisites

Participants need to have successfully completed the IoT Advanced course with Cognixia or have equivalent skills as below:
  • Familiarity with IoT terminologies
  • Knowledge of IoT device design/knowledge of prototyping using open source prototyping boards like Arduino
  • Knowledge of sensors, microcontroller and communication technologies like Wi-Fi
  • Conceptual knowledge of networking and internet communication
  • Basic knowledge of architecting IoT solution

Curriculum

  • Internet of Things – history and evolution
  • IoT use-cases
  • Economic potentials
  • Future trends
  • IoT Network and Device
  • Platform and Application Architecture
  • Open source initiatives
  • Industry 4.0 – Reference Architecture
  • Reference Architectural Model of Industry 4.0 – IIRC, Industrial Internet Consortium (IIC), Industrial Internet Reference Architecture (IIRA)
  • Understanding Data, Information, Knowledge and Wisdom (DIKW Pyramid)
  • Types of data
  • Physical and logical representation of data
  • Natural languages – Symbolic representation
  • Computer languages – Data encoding, storage and interpretation
  • Structured and unstructured data
  • IoT Devices – Sensor and control nodes
  • Data collection & processing – Edge & Cloud
  • Web services architecture and protocols – HTTP & MQTT
  • Understanding IoT enterprise architecture
  • Data aggregation, processing and analytics at the edge
  • Addressing IT and OT integration
  • IoT brokers
  • AWS Greengrass and Azure IoT edge solutions
  • Selection of sensor to capture right set of data
  • Handling of sensor data
  • Analog to digital conversion
  • Event detection
  • Data pre-processing
  • Integration of different data sources
  • Heterogeneity and distributed nature
  • Limitations of Sensor Nodes
  • Real-Time/Streaming Analytics , Descriptive, Diagnostic, Predictive and Prescriptive
  • Analytics/Machine Learning using Python advance packages: NumPy, SciPy, Matplotlib, Pandas and Sci-kit learn
  • Python: History and background
  • Python IDEs
  • Anaconda Python distribution
  • Introduction
  • Setup and getting started
  • Data handling in Python
  • Dynamic typing feature of Python
  • Sequences and data structures – Strings, lists, tuples, dictionary and sets
  • Arithmetic assignment
  • Comparison
  • Logical (or Relational) operators
  • Conditional (or ternary) operators
  • If , If-elif structures
  • While and For loops
  • The range() Function
  • Break and Continue Statements, and Else clauses on Loops
  • Pass statement
  • Local variables
  • Default argument values and Returning values
  • Keyword & Positional arguments
  • Arbitrary argument lists
  • Documentation strings
  • Unpacking argument lists
  • Lambda functions
  • List comprehension
  • Map, apply, reduce & filter
  • Opening a File
  • Reading from a file
  • Writing to a file
  • Closing a File
  • File handling using With statement
  • Reading directories & other basic directory operations (getcwd, mkdir, chdir etc.)
  • Renaming & deleting files
  • Building modules
  • Executing modules as scripts, The Module Search Path
  • ‘Compiled’ Python files
  • Standard Modules
  • The dir() Function
  • Packages
  • OOPs fundamentals
  • Class definition syntax, Class objects, Instance objects, Method objects; Instantiation
  • Data Member – Class variable/Instance variable
  • Function and Operator overloading
  • Inheritance
  • Handling Exceptions
  • Try-except, Else clause and Finally clause
  • Raising Exceptions
  • User-defined Exceptions
NumPy
  • One-dimensional arrays, Multi-dimensional arrays
  • NumPy arrays compared to Python lists
  • Modifying parts of an array
Pandas 
  • Series and DataFrames
  • Accessing elements from a series
  • Series alignment
  • Element-wise operations
  • Creating a DataFrame from NumPy Array, Series CSV files
  • Getting columns and rows
  • Data wrangling
IoT data – Descriptive analytics using Pandas  Plotting with Matplotlib and Seaborn
  • What is Machine Learning?
  • Introduction to Machine Learning
  • Types of Machine Learning
  • Basics of statistics and linear algebra
  • Supervised machine learning – Regression, Classification
  • Unsupervised learning – Clustering
  • Dimensionality reduction
  • Model performance evaluation
  • Time series analysis – IoT data
  • Predictive maintenance IoT system application and case study
Public, Private and Hybrid cloud platforms and deployment strategy IaaS, SaaS, PaaS models Cloud components and services
  • Device connectivity & management
  • Cloud brokers
  • Rules Engines
  • Databases
  • Visualization
  • Reporting
  • Notification/Alarm management
Example platforms: AWS IoT, Microsoft Azure
  • Overview of security and privacy in Information System
  • Principles of IoT security
  • IoT security guidance
  • Identify the known threats, risks, vulnerabilities and privacy issues
  • Security architectures

Interested in this course?

Reach out to us for more information

+91-7227048672
Talk to us
inquiry@cognixia.com

Course Feature

Course Duration
24x7 Support
Lifetime LMS Access
Price match Guarantee

FAQs

Anyone keen to excel in the world of IoT technologies and build a career as an IoT Expert should take up this training with Cognixia.
Our trainers are subject matter experts in the field of IoT Architectures, IoT Data Analytics, Machine Learning using Python, IoT Cloud Platforms and IoT Security
When you enroll for this course, you get lifetime access to our Learning Management System (LMS) which would be your one-stop destination to access class recordings, presentations, sample codes, projects and lots of other learning material. Even if you miss a session, a recording of that session, as well as all the other sessions would be available on the LMS that you can access anytime, anywhere.
An internet speed of at least 2 Mbps is essential.
For any queries, you can reach out to our technical support team and they will guide you accordingly.
Once all the sessions of the course are completed, you will be evaluated on the basis of multiple parameters such as your attendance in the sessions, your scores in multiple-choice questions based assessment, etc. Based on your overall performance, you will receive a course completion certificate.
You need to be through with the prequisites already mentioned. A customized tool kit to carry out your IoT projects will be provided.

Reviews