Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study, Virtual Classroom, Campus Based/Physical Classroom | Video and Text Based |
Course Overview
The candidates who are interested in exploring the area related to the field of the internet of things should definitely take this Post Graduate Programme in the Internet of Things training. The course offered here by the BITS Pilani is a 100 percent online learning programme. Participants who are interested to pursue this course should spend at least 11 months completing the Post Graduate Programme on the Internet of the Things certification syllabus. Experienced faculty in the field will train the students on how they can deal with the situations and problems at the work front professionally. Learners will be able to learn through a 6-week capstone project where they can learn from the practical exposure provided by the faculty.
The enrolled candidates will be able to gain hands-on experience at the campus by pursuing the Post Graduate Programme in the Internet of Things programme. Campus immersion modules will help the candidates to learn and interact with their colleagues as well as with their faculty. Participants who will be able to complete this course successfully will become a vital part of BITS Pilani alumni. Faculty may teach the students the course of the course through interactive learning such as live lectures, discussions, and projects.
The Highlights
- Online programme
- Duration of this course 11 months
- Certification available
- EMI option available with 0 percent interest
- Practical exposure through remote labs
- 6-week capstone project
- 2 campus immersion modules of 2 days each
- Alumni status of BITS Pilani
Programme Offerings
- Live Lectures
- discussions
- Projects
- Modules
- assignments.
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | BITS Pilani |
- The Post Graduate Programme in Internet of Things fees is Rs.2,45,000.
- Payable fees include GST.
- Candidates need to pay a block amount of Rs.25,000.
Post Graduate Programme in Internet of Things fees details
Fee Category | Amount in INR |
Block amount | Rs. 25,000 |
Registration amount | Rs. 2,20,000 |
Eligibility Criteria
Education
Candidates who are interested in pursuing this course should hold degrees such as BE/B.Tech, computer science, informatics systems/electronics/electrical/instrumentation for pursuing this Post Graduate Programme in Internet of Things Birla Institute of Technology, Pilani
Certification qualifying details
Candidates who want to achieve the Post Graduate Programme in Internet of Things certification for this course should complete all the modules, projects, and assignments in order to gain certification for this course.
What you will learn
After pursuing the Post Graduate Programme in Internet of Things programme candidates will be able to learn about-
- Candidates will be able to learn about how to design and implement IoT applications that can manage big data.
- Students may acquire knowledge on how to integrate geographically distributed devices with diverse capabilities under the Post Graduate Programme in Internet of Things online course.
- Through this curriculum of Post Graduate Programme in Internet of Things certification syllabus, candidates can learn about how to make connections with the cyber world with the physical world of humans, factories and automobiles.
- Participants can gain information about accessing, selecting and customizing technologies for IoT applications under this Post Graduate Programme in Internet of Things certification course.
- Students are able to use processors and peripherals to design and build IoT hardware.
- Aspirants may understand the building blocks of IoT technology.
- The enrolled candidates can explore the vast spectrum of IoT applications.
- Experienced faculty will teach the candidates tips and tricks required for being successful in the field.
- Students are able to learn about the software and hardware components of IoT.
- Candidates can design applications for IoT applications as a part of the Post Graduate Programme in Internet of Things certification benefits.
Who it is for
The course is highly recommended for -
- Engineers who desire to switch to IoT career opportunities.
- Candidates who wish to IT, Logistics, Telecom, Energy and Manufacturing
Admission Details
Candidates will get admission in the Post Graduate Programme in Internet of Things by following these steps-
Step 1: Candidates should click on the link provided below https://bits-pilani-wilp.ac.in/certification-programmes/pgp-internet-of-things-iot.php
Step 2: Students are now required to create their accounts by registering their email ids respectively.
Step 3: The application form has to be filled by the candidates.
Step 4: Candidates will receive the provisional admission letter 2 days after submitting their application forms.
Step 5: After that participants should now submit an application fee, passport size photograph, id proof, and employment proof at the online application centre.
Step 6: Once all the formalities are successfully completed participants will be able to receive the final admission offer letter.
Step 7: Participants now have to submit the first installment and they will receive the student ID as well as the program schedule.
The Syllabus
- Healthcare
- Transportation and logistics
- E-governance
- Industry 4.0
- Smart agriculture
- Home automation
- Smart city
- Analytics in the cloud
- Hands-on exercises
- Introduction to IoT analytics on AWS
- Complex event processing
- Setting up flink in the cloud
- Real-time stream analysis
- Data analysis using spark and spark ML
- Introduction to apache-spark
- Setting up Hadoop on AWS
- Offline, batch analysis using Hadoop
- Introduction to Hadoop
- Offline analysis
- Guidelines for visuals
- Dashboard setup
- Connecting streams with dashboard
- HTML5 canvas and inline SVG
- Overview of D3.js
- Predictive models for real-time data
- Hands-on exercise using python libraries
- Monitoring outlier detection and change detection
- Forecasting with models
- Models for real-time data
- Processing streaming data
- Complex event processing with Flink
- Introduction of link
- Complex event processing
- Processing teams with kineses
- Introduction to AWS kinesis
- Processing streams with storm
- Introduction to storms
- Windows sampling
- Approximate answers
- Overview of data processing
- Streaming data
- Hands-on exercises
- Introduction to kafka
- Data flow management in the streaming analysis
- Real-time streaming architectures
- Characteristics
- Images, videos, and speech analysis
- Introduction to TensorFlow, Keras, OpenCV
- Concepts and principles
- Time series analysis
- Linear models on time series
- Stationary, non - stationary models
- Windows smoothing
- Introduction to orange, visual programming tools for ML
- Introduction to python sci-kit library
- Techniques for clustering
- Techniques for classification and prediction
- Techniques for identifying associations and correlations
- Introduction to orange, visual programming tool
- Introduction to python matplotlib
- Sine and Mukti variable analysis
- Exploring numeric and categorical data
- Dealing with duplicates, null values, data, and time values
- Introduction to python pandas library
- RFID data cleaning: data redundancy
- RFID data cleaning: missed data reading and unmissed dta reading
- Characteristics of RFID data
- Sensor data cleaning
- Regression models
- Probabilistic models: an online bayesian approach
- Sources of error
- Data acquisition and in distributed sensor network
- Approaches for data acquisition
- Query optimization issues
- Query processing in distributed sensor networks
- Data models for distributed sensor networks
- Characteristics of distributed sensor networks
- Python basics
- Classes
- Data structure: lists, tuples, sets, dictionaries
- Strings, functions, lambda functions, modules, packages
- Variables, identifiers, operators, expressions, control statements
- Process of data analytics
- Demonstrations
- Overview of AWS services for IoT data
- Technologies of data analytics
- Role of analytics in IoT
- Challenges in managing IoT data
- Demonstrations of time series, Dynamo DB
- Technologies for the challenges
- Challenges in the data processing
- Challenges in data storage
- Data in IoT
- Types of IoT data
- Data sources in IoT data
- Data life cycle in IoT with examples
- Actuators
- Actuators - motion control, motor control, relays, solenoid valve, and interface with microcontroller
- Advanced sensing technologies
- HCI - human-computer interfacing
- BCI - brain-computer interface introduction, types, classification, techniques
- Multi-sensor fusion
- Data compression/decompression
- Algorithms and techniques
- Raw sensor data processing
- Signal conditioning and processing
- Sampling, synchronization
- Sensors
- Case studies for different application domains
- Acoustic sensors
- Photosensors
- Biometric sensors
- Motion sensors
- Proximity sensors
- Sensor categories
- Commonly used software development technologies in IoT
- Commonly used OS
- Commonly used tools
- Commonly used software development technologies and languages in IoT
- Developing applications on Raspberry Pi
- Sensor-based IoT applications development on raspberry pi
- Set up raspberry pi
- Developing applications on raspberry pi
- Server-side application development
- Implementing RESTful services [SOAP/CoAOP]
- Web server implementation and deployment
- Android client development
- Device database
- Working with SQLite database
- Connectivity
- Communication over internet
- Communication over Bluetooth
- Activities and intents
- Intents
- Activities
- UI components
- Buttons
- Views
- Layouts
- android application architecture
- Introduction to the operating system
- Interprocess communication and shared memory
- Remote procedure calls
- Shared memory
- Locks
- Task priority and critical ability
- Real-time scheduling algorithms
- Components of operating systems
- I/O communications
- Memory model
- Process, thread concurrency
- IoT software architecture
- Modular architecture
- Life cycle model
- Network security privacy
- Security solutions
- Security attacks
- Issues and challenges
- Common network standards
- Summary [case studies]
- Vehicular networks [CAN, Modbus, Ethernet/industrial protocol, MQTT, TTP/C, flexray]
- Industrial networks
- Industrial and automotive networks
- Other standards
- DSRC. WAVE
- LORA
- NFC
- 802.15.4 and variants
- Bluetooth and variants
- 802.11 and variants
- Network models and architecture
- Interfacing to structured networks - broadband, cellular, satellite
- VANET
- MANET
- WSN
- Ad Hoc
- Introduction to NS2
- Communication models
- P2P
- Publisher subscriber
- Client-server
- Introduction to networking
- Wired and wireless networks
- TCP/IP stack
- Communication and networking requirements in IoT
- Introduction - application 1- fitness tracking system
- Network hierarchy
- Network requirements
- Application overview
- Power consumption and management
- Low power modes
- Dynamic techniques
- Static techniques
- Power management
- Energy consumption analysis
- I/O
- DMA
- Interrupts
- Polling
- Modes of transfer
- I/O devices
- ISP/IAP
- Storage devices
- Ports
- Buses
- Peripheral blue - USB, SPI, 112C, UART
- System bus - AMBA
- On-chip buses
- Clock, timing, interrupt
- Interrupt handlers
- Priority logic
- Interrupt latency, jitter
- Interrupts
- Timers
- Platform-specific and non - specific
- Clocking and clock gating
- Memory
- Memory protection
- Memory management unit
- Cache
- Cache consistency and coherence models
- Cache architectures
- Cache hierarchy
- IoT platforms
- ARM architecture - superscalar
- ARM architecture - scale
- IoT platforms
- Introduction to IoT applications
- Microprocessors and microcontrollers for IoT
- Parallel architectures [ILP, DLP, TLP]
- IoT applications
- Smart home exchange
- Genetic architecture
- Introduction
- Smart environments - industrial application [process control] home automation
- Challenge in design and environments
- Enabling technologies
- Overview
- Transportation and logistics - applications
- Building blocks - OS
- Building blocks - cloud and communication
- Building blocks - sensors
- Building blocks - processor
- Genetic fitness tracking system architecture
- Key design challenges
- Health care example - fitness tracking systems
- Challenges in design and development
- Enabling technologies
- Overview
- IoT system design examples
- Weather monitoring system - hardware and software design, implementation with demo
- Smart lighting system - hardware and software design, implementation with demo
- IoT network and cloud services
- Introduction to a cloud services model
- IoT communication APIs - REST and web socket
- Link, networking, transport, and application layer protocol
- IoT platforms and end devices
- Raspberry communication interfaces
- Raspberry PI I/O interfaces
- Introduction to OS and programming languages for IoT end devices
- Raspberry architecture
- Introduction to IoT physical endpoints and platforms
- Design methodology and lifecycle
- Example of level 6 system - weather monitoring
- Design methodology - IoT reference model A
- Example level 1 system - smart lighting
- Design methodology - IoT reference model A
- IoT - introduction
- Different levels of IoT applications - level 1 - 6 with examples
- IoT enabling technologies - physical emd points, network services, cloud
- Introduction to IoT and cyber-physical systems