Euroasia Training Euroasia Training
AR

Data Analytics, Artificial Intelligence and Decision Sciences

IoT Data Handling and Real-Time Analytics

5 units

Please select a city/session before registration.

About this program

In today’s connected world, organizations produce vast streams of IoT data that demand immediate analysis. This Real-Time Analytics and IoT Data Processing Training Course familiarizes participants with the key technologies, frameworks, and models essential for managing data in motion. Attendees will delve into real-time analytics solutions, IoT system designs, and machine learning techniques applied to streaming data. Practical exercises and industry case studies from sectors like manufacturing, healthcare, and transportation will demonstrate how real-time data drives enhanced performance, risk mitigation, and accelerated innovation. Upon completion, participants will be equipped to design, implement, and oversee real-time analytics strategies that fully leverage IoT data.

Course benefits

  • Gain a solid foundation in real-time analytics and IoT concepts
  • Effectively process and analyze streaming data
  • Apply artificial intelligence and machine learning models within real-time contexts
  • Enhance the efficiency and responsiveness of IoT ecosystems
  • Foster innovation through immediate data-driven decision-making

Key outcomes

  • Investigate technologies for processing data in real time
  • Architect IoT systems optimized for continuous analytics
  • Utilize stream processing frameworks such as Kafka, Spark, and Flink
  • Implement machine learning models for detecting anomalies in IoT streams
  • Design systems that ensure scalability, reliability, and minimal latency
  • Address security and privacy challenges associated with IoT data streams
  • Incorporate real-time analytics into broader organizational strategies

Who should attend

  • Data engineers and IoT domain experts
  • Business executives in manufacturing, logistics, or smart system sectors
  • IT managers and operational staff
  • Analysts seeking to explore real-time data applications

Course outline

1

Unit 1: Fundamentals of IoT and Real-Time Analytics

  • The significance of IoT in contemporary enterprises
  • Core principles of real-time data analytics
  • Advantages and obstacles of processing data streams
  • Illustrative examples of insights driven by IoT
2

Unit 2: Frameworks and Architectures for IoT Data

  • Developing architectures for IoT systems
  • Techniques for data ingestion and processing workflows
  • Technologies for stream processing (Kafka, Spark, Flink)
  • Hands-on exercise with IoT data pipelines
3

Unit 3: Leveraging Machine Learning with Real-Time Data

  • Implementing ML within streaming data contexts
  • Real-time anomaly detection and forecasting insights
  • Edge computing and intelligence at the device level
  • Case studies showcasing AI-enhanced IoT applications
4

Unit 4: IoT Data Security, Privacy, and Governance

  • Maintaining data integrity and security in real time
  • Privacy issues related to IoT data transmission
  • Adhering to regulatory requirements and standards
  • Establishing confidence in IoT-enabled platforms
5

Unit 5: Future Directions and Strategy for Real-Time Analytics

  • Incorporating real-time analytics into organizational strategy
  • Scaling architectures for extensive IoT data volumes
  • New developments in IoT and streaming data analytics
  • Projected trends for real-time data frameworks