Certified Internet of Things Professional Program (CIoTP)
Introduction and Overview
The Internet of Things (IoT) is beginning to grow significantly, as consumers, businesses, and governments recognize the benefit of connecting IoT devices to the internet. We are entering the era of the connected business model. Potentially, any asset, person, product, process, or source of data can be connected, making a reality of the long forecast connected house, car, factory and ecosystem. Smaller and cheaper sensors—embedded technology in products like cars, oil wells, wearables, and business processes—will become the norm over the next decade. All this connectedness has the potential to radically change business models and the competitive landscape.
According to BI Intelligence report, The Internet of Things will be the largest device market in the world. We estimate that by 2019 it will be more than double the size of the smartphone, PC, tablet, connected car, and the wearable market combined.
The IoT will result in $1.7 trillion in value added to the global economy in 2019. This includes hardware, software, installation costs, management services, and economic value added from realized IoT efficiencies. Device shipments will reach 6.7 billion in 2019 for a five-year CAGR of 61%. Revenue from hardware sales will be only $50 billion or 8% of the total revenue from IoTspecific efforts, as software makers and infrastructure companies will earn the lion’s share.
The enterprise sector will lead the IoT, accounting for 46% of device shipments this year, but that share will decline as the government and home sectors gain momentum. By 2019, government will be the leading sector for IoT device shipments.
This course will focus on the core technologies behind Internet of Things. This certification leverages and explores the middleware for IoT, Machine learning for Intelligent IoT, Data Science for Intelligent IoT, Analytic engine for IoT, Big data platform for IoT, API design considerations for IoT, IoT standards and Management of IoT, which makes a world fully connected.
After the course, participants will have a good understanding of the different pieces of an IoT system and how they interact.
Bringing together these skills and the related specific knowledge on the subject will allow the project partners to tackle all aspects of considerable complexity of developing a general architecture for the Internet of Things.
Duration
- 32 Hours (4 Days)
Who Should Attend?
- Data Analyst - Statistics and Mining
- Senior Data Analyst – Statistics and Mining
- Data Analyst - Text Analytics
- Big Data Analyst
- Data Scientist
- Operations Research Analyst
Pre-Requisite
Participants are recommended to have preferably min. 2 years of experience in software development, business domain or data/business analysis.
Program Structure
This is a 4-day intensive training program with the following assessment components.
Component 1: Written Examination (MCQ)
Component 2. Project Work Component (PWC)
These components are individual based. Participants will need to obtain 70% in both the components in order to qualify for this certification. If the participant fails one of the components, they will not pass the course and have to re-take that particular failed component. If they fail both components, they will have to re-take the assessment.
Course Outcomes
- Acquire the knowledge and skills on Business Overview of IoT system
- Design and develop smart IoT Applications using Carriots, ThingSpeak
- Understand the Functional Components and Classification of the IoT Middleware
- Acquire the knowledge on Machine Learning for intelligent IoT
- Acquire the skills on Data Science, Analytic engine and Big data platform in IoT
- Understand the API Design Considerations for the IoT
- Learn Machine Learning concepts and techniques through an open source analytical tool
Course Outline
Unit 1: Business Overview of IoT
- Case Studies from Industry Leaders
- Smart house and smart city
- Smart Cars
- Business Rule generation for IoT
Unit 2: Middleware for IoT
Functional Components of an IoT-middleware
- Interoperation
- Context detection
- Device discovery and management
- Security and privacy
- Managing data volume
Classification of the IoT-middleware
Unit 3: Machine learning for intelligent IoT
- Introduction to Machine learning
- Machine learning tools and techniques
- Decision Tree Learning
- Artificial Neural Networks
- Association Rule Learning
- Fraud and alert analytic through IoT
- Case Study
- Demonstration of Machine learning techniques Using RapidMiner and R
Unit 4: Data Science for intelligent IoT
- An Overview of Data Science
- What is Data Science
- Extracting meaning from data
- Techniques to acquire data
- Handling large scale data
Addressing IoT problems in Data science
Case Study
Unit 5: Analytic Engine for IoT
- Insight analytic
- Predictive analytic
- Visualization analytic
- Pattern detection
- Root cause discovery
Unit 6: Big Data Platform for IoT
- Characteristics of Big Data
- Why Big Data is important in IoT
- Fundamental of Map reduced system (MR)
- Hadoop and HDFS
- Apache Kafka
- Apache Storm
- Distributed database - MongoDB
Unit 7: API Design Considerations for the IoT
- Know Your API Requirements
- Understand Your Audience
- Decrease Confusion For The User, Let Provider Handle Complexity
- Use Hypermedia For Evolvability
- Learn From Real World Information Design
- API Design Needs To Convince the Architect
Unit 8: IoT Standards
Types of Standards
- Sensor network standards
- IoT foundational standards
- Domain-specific standards
Quality and Standards Infrastructure
SPRING’s Achievements in Quality and Standards
Unit 9: Management of IoT
- Management System/Architecture
- Management Protocols
- Configuration Management
- Monitoring
- Security
- Energy Management
Hands-On
Participants will have guided hands-on sessions on IoT to get firsthand knowledge about IoT and backbone technologies (business analytics, big data etc.) needed to build IoT applications. During this session they will gain understanding of several factors in building a successful IoT system.
The program consists of two hands-on sessions, three hours duration each. Also, participants will have demonstration session on RapidMiner and ThingSpeak.
Hands-on 1: Participants will also perform business analytics on an IoT application data (including data
loading and analysis operations) using an open source BA tool R.
Hands-on 2: Setup and configure an IoT platform such as Carriots. Further, participants will also
perform building applications and managing devices connected to Internet.
Written Assessment
As part of the written examination, each participant will be assessed individually on the last day
of the training for their understanding of the subject matter and ability to evaluate, choose and
apply them in specific context and also the ability to identify and manage risks. The assessment
focuses on higher levels of learning in Bloom’s taxonomy: Application, Analysis, Synthesis and
Evaluation..
This written examination will primarily consist of 40 multiple choice questions spanning various
aspects as covered in the program. It is an individual, competency-based assessment.
Exam Preparation
The objective of the certification examination is to evaluate the knowledge and skills acquired by the participants during the course. The weightage in key topics of the course as follows:
- Business Overview of IoT [8%]
- Middleware for IoT [8%]
- Machine Learning for intelligent IoT [16%]
- Data Science for intelligent IoT [16%]
- Analytic engine for IoT [10%]
- Big data platform for IoT [16%]
- API Design Considerations for the IoT [8%]
- IoT standards [8%]
- Management of IoT [10%]
Software Used
- Carriots
- R
- RapidMiner
- ThingSpeak