IoT Programming with Python Training Course

Technological advancements and the surge in information are revolutionizing business operations across various sectors, including government. The rise of mobile devices and applications, smart sensors and gadgets, cloud computing solutions, and citizen-centric portals is driving up data generation and digital archiving rates within governmental bodies. As digital information grows more extensive and intricate, managing, processing, storing, securing, and disposing of it becomes increasingly complex. New tools for capturing, searching, discovering, and analyzing unstructured data are aiding organizations in extracting valuable insights. The government sector is at a pivotal moment, recognizing that information is a strategic asset and that both structured and unstructured data must be protected, utilized, and analyzed to better serve citizens and meet mission requirements. As governmental leaders work towards transforming their organizations into data-driven entities to achieve their missions effectively, they are setting the stage for correlating dependencies across events, individuals, processes, and information.

High-value government solutions will emerge from a blend of disruptive technologies:

• Mobile devices and applications
• Cloud services
• Social business technologies and networking
• Big Data and analytics

IDC forecasts that by 2020, the IT industry will reach $5 trillion, which is approximately $1.7 trillion more than today’s figures, with around 80% of this growth attributed to third-platform technologies. In the long run, these technologies will be essential for managing the complexity brought about by increased digital information. Big Data stands out as one of the intelligent industry solutions, enabling governments to make informed decisions based on patterns identified through the analysis of large volumes of data — whether related or unrelated, structured or unstructured.

However, achieving these goals requires more than just accumulating vast amounts of data. "Interpreting such extensive Big Data necessitates cutting-edge tools and technologies capable of analyzing and extracting valuable insights from diverse information streams," as noted by Tom Kalil and Fen Zhao in a post on the White House Office of Science and Technology Policy blog.

The White House took a significant step towards assisting agencies in finding these technologies when it launched the National Big Data Research and Development Initiative in 2012. This initiative allocated over $200 million to maximize the potential of the Big Data explosion and the tools required for its analysis.

Big Data presents challenges that are nearly as formidable as its promise is promising. Efficient data storage is one such challenge. With tight budgets, agencies must reduce per-megabyte storage costs while ensuring easy access to the data when needed. Backing up large volumes of data adds another layer of complexity.

Effectively analyzing the data is also a significant hurdle. Many agencies use commercial tools that help them sift through vast amounts of data and identify trends that can enhance operational efficiency (a recent MeriTalk study found that federal IT executives believe Big Data could save more than $500 billion while fulfilling mission objectives).

Custom-developed Big Data tools are also aiding agencies in analyzing their data. For instance, the Oak Ridge National Laboratory’s Computational Data Analytics Group has made its Piranha data analytics system available to other agencies. This system has helped medical researchers identify a link that can alert doctors to potential aortic aneurysms before they occur. It is also used for more routine tasks like matching job candidates with hiring managers by sifting through résumés.

Insurtech (also known as Digital Insurance) represents the integration of insurance with new technologies. In this sector, "digital insurers" leverage technological advancements to refine their business and operational models, thereby reducing costs, enhancing customer satisfaction, and increasing operational flexibility.

This instructor-led training will equip participants with knowledge about the technologies, methodologies, and mindset required for digital transformation within their organizations and across the industry. The course is designed for managers who need to grasp a comprehensive overview, dispel myths and technical jargon, and initiate steps towards formulating an Insurtech strategy.

Upon completion of this training, participants will be able to:

• Discuss Insurtech and its various components in a knowledgeable and structured manner
• Identify and clarify the role of each key technology within Insurtech
• Create a general strategy for implementing Insurtech within their organization

Audience

• Insurance professionals
• Tech experts in the insurance sector
• Insurance industry stakeholders
• Consultants and business analysts

Course Format

• A combination of lectures, discussions, exercises, and group activities involving case studies

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level IT professionals and business managers who wish to understand the potential of IoT and edge computing for enabling efficiency, real-time processing, and innovation in various industries.

By the end of this training, participants will be able to:

• Understand the principles of IoT and edge computing and their role in digital transformation.
• Identify use cases for IoT and edge computing in manufacturing, logistics, and energy sectors.
• Differentiate between edge and cloud computing architectures and deployment scenarios.
• Implement edge computing solutions for predictive maintenance and real-time decision-making.

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level developers, system architects, and industry professionals who wish to leverage Edge AI for enhancing IoT applications with intelligent data processing and analytics capabilities.

By the end of this training, participants will be able to:

• Understand the fundamentals of Edge AI and its application in IoT.
• Set up and configure Edge AI environments for IoT devices.
• Develop and deploy AI models on edge devices for IoT applications.
• Implement real-time data processing and decision-making in IoT systems.
• Integrate Edge AI with various IoT protocols and platforms.
• Address ethical considerations and best practices in Edge AI for IoT.

This instructor-led, live training in the UAE (online or onsite) is aimed at product managers and developers who wish to use Edge Computing to decentralize data management for faster performance, leveraging smart devices located on the source network.

By the end of this training, participants will be able to:

• Understand the basic concepts and advantages of Edge Computing.
• Identify the use cases and examples where Edge Computing can be applied.
• Design and build Edge Computing solutions for faster data processing and reduced operational costs.

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level professionals who wish to apply Federated Learning to optimize IoT and edge computing solutions.

By the end of this training, participants will be able to:

• Understand the principles and benefits of Federated Learning in IoT and edge computing.
• Implement Federated Learning models on IoT devices for decentralized AI processing.
• Reduce latency and improve real-time decision-making in edge computing environments.
• Address challenges related to data privacy and network constraints in IoT systems.

In this instructor-led, live training in the UAE, participants will learn the fundamentals of IoT as they step through the creation of an Arduino-based IoT sensor system.

By the end of this training, participants will be able to:

• Understand the principles of IoT, including IoT components and communication techniques.
• Learn how to use Arduino communication modules that can be used for different IoT systems.
• Learn how to use and program a mobile app to control Arduino.
• Use a Wi-Fi module to connect the Arduino to another device.
• Build and deploy their own IoT Sensor System.

In contrast to other technologies, IoT is significantly more intricate, encompassing nearly every branch of core engineering—Mechanical, Electronics, Firmware, Middleware, Cloud, Analytics, and Mobile. Each layer of its engineering has economic, standardization, regulatory, and evolving technological aspects. This course aims to comprehensively cover all these critical facets of IoT Engineering for the first time.

Summary

An advanced training program that delves into the current state-of-the-art in Internet of Things (IoT).

Covers multiple technology domains to develop an understanding of IoT systems and their components, highlighting how they can benefit businesses and organizations.

Demonstrates practical IoT applications across various industry sectors such as Industrial IoT, Smart Cities, Retail, Travel & Transportation, and connected devices through live examples.

Target Audience

This program is designed for managers overseeing business and operational processes within their organizations who wish to leverage IoT to enhance efficiency in systems and workflows.

Entrepreneurs and investors interested in launching new ventures can gain a deeper understanding of the IoT technology landscape to effectively utilize it in their initiatives.

The Internet of Things (IoT) market is projected to be massive, given its integration across consumer, business-to-business, and government sectors. It currently accounts for 1.9 billion devices and is expected to reach 9 billion by 2018, equating roughly to the combined total of smartphones, smart TVs, tablets, wearable computers, and PCs.

In the consumer sector, numerous products and services have already transitioned into IoT, including kitchen appliances, home automation systems, parking solutions, RFID technologies, lighting, heating systems, and Industrial Internet applications.

While the foundational technologies of IoT are not new—M2M communication has existed since the inception of the internet—the recent surge in inexpensive wireless technologies, coupled with widespread adoption of smartphones and tablets, has fueled the current demand for IoT solutions. The proliferation of mobile devices has driven this growth.

The vast opportunities within the IoT sector have attracted a significant number of small and medium-sized entrepreneurs to invest in IoT ventures. Additionally, the rise of open-source electronics and IoT platforms has made it increasingly affordable to develop and manage IoT systems at scale. Existing electronic product owners are under pressure to integrate their devices with internet or mobile applications.

This training is aimed at providing a comprehensive overview of an emerging industry for IoT enthusiasts and entrepreneurs, covering both technological and business aspects.

Course Objective

The primary goal of the course is to introduce new technological options, platforms, and case studies related to IoT implementation in areas such as home & city automation (smart homes and cities), Industrial Internet, healthcare, government sectors, Mobile Cellular, and more.

A basic introduction to all elements of IoT—Mechanical, Electronics/sensor platform, Wireless and wireline protocols, Mobile-to-Electronics integration, Mobile-to-enterprise integration, Data-analytics, and Total control plane.

Exploration of M2M wireless protocols for IoT—WiFi, Zigbee/Zwave, Bluetooth, ANT+: Understanding the appropriate use cases for each protocol.

Demonstration of mobile/desktop/web apps for registration, data acquisition, and control—available M2M data acquisition platforms like Xively, Omega, NovoTech, etc.

Discussion on security issues and solutions in IoT.

Overview of open-source/commercial electronics platforms for IoT such as Raspberry Pi, Arduino, ArmMbedLPC, etc.

Review of open-source/commercial enterprise cloud platforms like AWS-IoT apps, Azure-IOT, Watson-IOT cloud, along with other minor IoT clouds.

Analysis of business and technology aspects of common IoT devices such as home automation systems, smoke alarms, vehicles, military applications, and home health solutions.

In contrast to other technologies, IoT is significantly more intricate, encompassing nearly every branch of core engineering—Mechanical, Electronics, Firmware, Middleware, Cloud, Analytics, and Mobile. Each layer of its engineering involves aspects of economics, standards, regulations, and the evolving state of technology. This marks the first time a comprehensive course is being offered to cover all these critical facets of IoT Engineering.

For manufacturing professionals, the most crucial aspect is understanding advancements in Industrial Internet of Things (IIoT), which includes predictive maintenance, condition-based monitoring of machines, production optimization, energy efficiency, supply chain optimization, and maximizing uptime for manufacturing utilities.

Summary

• An advanced training program that covers the latest developments in IoT within smart factories.
• Covers multiple technology domains to develop an understanding of IoT systems and their components, aiding manufacturing managerial professionals.
• Livestream demonstrations of model IIoT applications for smart factories.

Target Audience

• Managers overseeing business and operational processes within their respective manufacturing organizations who wish to leverage IoT to enhance system and process efficiency.

Duration: 3 Days (8 hours/day)

The estimated market value for Internet of Things (IoT) is enormous, as it integrates a vast array of devices, sensors, and computing power across consumer, business-to-business, and government sectors. The IoT will account for an increasing number of connections: currently at 1.9 billion devices, this figure is expected to reach 9 billion by 2018, roughly equal to the combined total of smartphones, smart TVs, tablets, wearable computers, and PCs.

In the consumer sector, numerous products and services have already transitioned into IoT, including kitchen appliances, parking solutions, RFID systems, lighting and heating products, and various applications in Industrial Internet.

However, the foundational technologies behind IoT are not new; machine-to-machine (M2M) communication has existed since the inception of the internet. What has changed over the past few years is the proliferation of inexpensive wireless technologies, alongside the widespread adoption of smartphones and tablets in homes. The explosive growth of mobile devices has driven current demand for IoT.

Industrial IoT (IIoT) for manufacturing has been widely used since 2014, with numerous IIoT innovations emerging since then. This course will introduce all key aspects of these advancements in the Industrial IoT domain.

This training is designed to provide a technology and business overview of an emerging industry so that IoT enthusiasts or entrepreneurs can understand the basics of IoT technology and its business applications.

Course Objective

The primary goal of this course is to introduce emerging technological options, platforms, and case studies on IoT implementation in smart factories for manufacturing sectors.

1. Examination of the business and technology aspects of common IIoT platforms such as Siemens MindSphere and Azure IoT.
2. Exploration of open-source/commercial enterprise cloud platforms for AWS-IoT apps, Azure-IOT, Watson-IOT, Mindsphere IIoT Cloud, along with other minor IoT clouds.
3. Study of open-source/commercial electronics platforms for IoT including Raspberry Pi, Arduino, ArmMbedLPC, etc.
4. Discussion on security issues and solutions in IIoT.
5. Development of mobile/desktop/web applications for registration, data acquisition, and control.
6. Understanding M2M wireless protocols for IoT—WiFi, LoPan, BLE, Ethernet, Ethercat, PLC: when and where to use each one?
7. A basic introduction to all elements of IoT—mechanical, electronics/sensor platforms, wireless and wireline protocols, mobile-to-electronics integration, mobile-to-enterprise integration, data analytics, and total control plane.

The Internet of Things (IoT) is a network infrastructure that connects physical devices and software applications wirelessly, enabling them to communicate with each other and exchange data through network communications, cloud computing, and data capture.

In this instructor-led live training session, participants will learn the basics of IoT as they go through the process of creating an IoT sensor system using the Raspberry Pi.

By the end of this training, participants will be able to:

• Grasp the principles of IoT, including its components and communication methods
• Set up the Raspberry Pi specifically for IoT applications
• Create and deploy their own IoT Sensor System

Audience

• Hobbyists
• Hardware/software engineers and technicians
• Technical individuals across all industries
• Beginner developers

Course Format

• Mixed lecture, discussion, exercises, and extensive hands-on practice

Note

• The Raspberry Pi supports multiple operating systems and programming languages. This course will use the Linux-based Raspbian as the operating system and Python for programming. For a specific setup request, please contact us to arrange.
• Participants must purchase their own Raspberry Pi hardware and components.

Machine-to-Machine (M2M) involves the automatic exchange of data between interconnected mechanical or electronic devices.

In this instructor-led, live training in the UAE, participants will learn about the various aspects of NB-IoT (also known as LTE Cat NB1) as they develop and deploy a sample NB-IoT based application.

By the end of this training, participants will be able to:

• Identify the different components of NB-IoT and how to fit together to form an ecosystem.
• Understand and explain the security features built into NB-IoT devices.
• Develop a simple application to track NB-IoT devices.

The Raspberry Pi is a compact single-board computer.

In this instructor-led live training session, participants will learn how to configure and program the Raspberry Pi to function as an interactive and robust embedded system.

By the conclusion of this training, participants will be able to:

• Set up an IDE (integrated development environment) for optimal development efficiency
• Program the Raspberry Pi to control devices such as motion sensors, alarms, web servers, and printers
• Comprehend the architecture of the Raspberry Pi, including its inputs and connectors for additional devices
• Familiarize themselves with different programming languages and operating systems available
• Test, debug, and deploy the Raspberry Pi to address real-world challenges

Audience

• Software developers
• Hardware/software technicians
• Technical professionals across various industries
• Hobbyists

Course Format

• The course includes a mix of lectures, discussions, exercises, and extensive hands-on practice.

Note

• The Raspberry Pi supports multiple operating systems and programming languages. This course will utilize the Linux-based Raspbian operating system and Python programming language. For a specific setup, please contact us to arrange.
• Participants are responsible for purchasing their own Raspberry Pi hardware and components.

This instructor-led live training course introduces the necessary software, hardware, and step-by-step procedures for constructing a facial recognition system from the ground up. Facial Recognition is alternatively referred to as Face Recognition.

The lab equipment includes a Raspberry Pi, camera module, servos (optional), among others. Participants must procure these components independently. The software utilized encompasses OpenCV, Linux, Python, and other tools.

Upon completion of this training, participants will be able to:

• Install Linux, OpenCV, and additional software utilities and libraries on a Raspberry Pi.
• Configure OpenCV for capturing and detecting facial images.
• Explore various packaging options for deploying a Raspberry Pi system in real-world settings.
• Tailor the system to accommodate diverse use cases such as surveillance and identity verification.

Course Format

• A blend of lectures, discussions, exercises, and extensive hands-on practice.

Note

• Alternative hardware and software options include Arduino, OpenFace, Windows, etc. If you wish to use any of these alternatives, please contact us to make arrangements.

ThingsBoard is an open-source IoT platform that provides device management, data collection, processing, and visualization for your IoT solution.

In this instructor-led live session, participants will learn how to integrate ThingsBoard into their IoT solutions.

By the end of this training, participants will be able to:

• Install and configure ThingsBoard
• Grasp the core features and architecture of ThingsBoard
• Create IoT applications using ThingsBoard
• Integrate ThingsBoard with Kafka for telemetry data routing from devices
• Connect ThingsBoard with Apache Spark to aggregate data from multiple devices

Audience

• Software engineers
• Hardware engineers
• Developers

Course Format

• The course includes lectures, discussions, exercises, and extensive hands-on practice.

Note

• To request a customized training for this course, please contact us to arrange the details.