Internet of Things Training & Certification
4.3 /5- Offered byCognixia
Internet of Things Training & Certification at Cognixia Overview
Duration | 48 hours |
Mode of learning | Online |
Difficulty level | Intermediate |
Credential | Certificate |
Internet of Things Training & Certification at Cognixia Highlights
- Lifetime Learning Management System (LMS) access, Free IOT Kit
- A technical team dedicated to resolving your queries anytime, anywhere
- Certificate of Completion
- Gain expert-level knowledge of IoT technology and tools
Internet of Things Training & Certification at Cognixia Course details
- LIVE INSTRUCTOR-LED ONLINE TRAINING
- 24/7 SUPPORT
- LIFETIME LMS ACCESS
- PRICE MATCH GUARANTEE
- CERTIFICATE OF EXCELLENCE ON SUCCESSFUL COMPLETION OF TRAINING
- IT professionals
- Electrical engineer
- Electronics engineer
- Solution architects
- Software Developers
- Maintenance Engineer
- Service Engineer
- Embedded engineers
- Embedded developers
- RF Engineer
- Solution Architect Engineer
- Automation Engineer
- Telecom Engineers
- System Engineer
- The Internet of Things (IoT) is ushering in a new era in science and technology, which will forever change our personal as well as professional lives, our consumer habits, and the way we do business. With the fast-changing world, these latest inventions and innovations will become the norm by 2020, and we estimate more than 50 billion devices will be connected via the Internet. In order to create early adopters, we have introduced a one-of-kind course on ??Internet of Things,?? the next big thing in the IT industry.
Internet of Things Training & Certification at Cognixia Curriculum
Introduction to Internet of Things
Concept and definitions
Understanding IT and OT convergence: Evolution of IIoT & Industry 4.0
IoT Adoption
Business opportunities: Product + Service model
Use cases
Concept of Data, Information, Knowledge and Wisdom
Knowledge discovery process
DIKW pyramid and relevance with IoT
Microcontrollers: cost, performance, and power consumption
Industrial networks, M2M networks
Sensor Data Mining and Analytics
Transducer: Sensor and Actuator
Data acquisition, storage and analytics
Signals and systems
Real-time analytics
Edge analytics
Wireless Sensor Area Networks (WSAN): Evolution of M2M and IoT Networks and Technologies
Sensor nodes
WSN/M2M communication technologies
Topologies
Applications
Design and Development of IoT Systems
IoT reference architectures
IoT design considerations
Networks, communication technologies and protocols
Smart asset management: Connectivity, Visibility, Analytics, Alerts
Cloud Computing and Platforms
Public, Private and Hybrid cloud platforms and deployment strategy
Industrial Gateways
IaaS, SaaS, PaaS models
Cloud components and services
Example platforms: ThingSpeak, Pubnub, AWS IoT
IoT Security
Standards and best practices
Common vulnerabilities
Attack surfaces
Hardware and Software solutions
Open source initiatives
Analytics
Descriptive, Diagnostic, Predictive, and Prescriptive
Analytics using Python advance packages: NumPy, SciPy, Matplotlib, Pandas and Sci-kit learn
CASE STUDIES AND ROADMAP
Cold chain monitoring
Asset tracking using RFID and GPRS/GPS
Hands-on/Practical Exercises
Programming microcontrollers (Arduino, NodeMCU)
Building HTTP and MQTT based M2M networks
Interfacing Analog and Digital sensors with microcontroller to learn real-time data acquisition, storage and analysis on IoT endpoints and edges
Interfacing SD card with microcontroller for data logging on IoT end devices using SPI protocol
Interfacing Real-time clock module with microcontrollers for time and date stamping using I2C protocol
Python exercises to check quality of acquired data
Developing microcontroller based applications to understand event based real time processing and in-memory computations
Setting up Raspberry Pi as Gateway to aggregate data from thin clients
Python programming on Raspberry Pi to analyze collected data
GPIO programming using Python and remote monitoring /control
Pushing collected data to cloud platforms
Designing sensor nodes to collect multiple parameters (Temperature, Humidity, etc.)
Uploading data on local gateway as cache
Uploading data on cloud platforms
Monitoring and controlling devices using android user apps and Bluetooth interfaces
Building wireless sensor networks using WiFi
Sensor data uploading on cloud using GSM/GPRS
Device to device communication using LoRa modules
Remote controlling machines using cloud based apps
Remote controlling machines using device based apps through cloud as an intermediate node
Interfacing Raspberry Pi with AWS IoT Gateway service to exchange messages
Interfacing Raspberry Pi with PUBNUB cloud to understand publish/subscribe architecture and MQTT protocol
Data cleaning, sub-setting, and visualization
Set of Python exercises to demonstrate descriptive and predictive analytics
Case study
Hardware Kit
Development Boards
Electronic Components
Communication Modules
Other courses offered by Cognixia
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Internet of Things Training & Certification