Introduction

The Internet of Things (IoT) generates massive amounts of real-time data from interconnected devices, sensors, and machines. Data Science for IoT enables organizations to analyze, process, and extract actionable insights from this data, enhancing efficiency, automation, and decision-making across industries like smart cities, industrial automation, healthcare, energy, and predictive maintenance.

This 5-day intensive training will cover data ingestion, real-time analytics, edge computing, machine learning models for IoT data, and cloud deployment strategies, equipping participants with the skills to handle large-scale sensor data analytics and IoT-driven decision-making.

Objectives

By the end of this course, participants will:

1. Understand IoT ecosystems, data pipelines, and architecture.
2. Learn IoT data collection, processing, and real-time analytics.
3. Implement machine learning models for predictive maintenance, anomaly detection, and automation.
4. Work with edge computing and cloud platforms for IoT scalability.
5. Optimize IoT data storage, security, and privacy.
6. Integrate IoT analytics with AI-driven decision-making.
7. Build end-to-end IoT data science projects with Python, MQTT, and cloud services.

Who Should Attend?

• Data Scientists & Machine Learning Engineers
• IoT Engineers & Developers
• Big Data & Cloud Architects
• Industrial Automation & Smart City Professionals
• Cybersecurity & Network Engineers
• Anyone working with sensor data & real-time analytics

Course Outline (5 Days)

Day 1: Fundamentals of IoT & Data Science Workflows

Morning Session

• Understanding IoT Ecosystems

  • IoT architecture: Devices, gateways, cloud, and edge computing
  • IoT communication protocols: MQTT, CoAP, HTTP, LoRaWAN
  • Hands-on: Connecting and streaming data from IoT devices

• IoT Data Collection & Storage

  • Data types: Sensor, time-series, geospatial, event-driven
  • Data ingestion using Kafka, RabbitMQ, and MQTT brokers
  • Hands-on: Streaming IoT data into a real-time database

Afternoon Session

• Exploratory Data Analysis (EDA) for IoT

  • Handling high-frequency time-series data
  • Visualizing IoT data trends and anomalies
  • Hands-on: IoT data wrangling and feature engineering

• Hands-on Exercise

  • Preprocessing environmental sensor data for anomaly detection

Day 2: Real-Time Analytics & Edge Computing

Morning Session

• Stream Processing for IoT

  • Overview of Apache Kafka, Spark Streaming, and Flink
  • Handling high-velocity IoT data streams
  • Hands-on: Building a real-time IoT data pipeline with Kafka

• Edge Computing vs. Cloud Computing

  • Edge AI and on-device processing
  • Using TensorFlow Lite and ONNX for edge-based ML
  • Hands-on: Deploying a machine learning model on an edge device (Raspberry Pi)

Afternoon Session

• Time-Series Forecasting for IoT

  • Moving averages, ARIMA, and Prophet models
  • Handling sensor drift and missing data
  • Hands-on: Predicting air quality trends using time-series models

• Hands-on Exercise

  • Deploying real-time IoT analytics for predictive maintenance

Day 3: Machine Learning for IoT Applications

Morning Session

• Predictive Maintenance & Anomaly Detection

  • Supervised vs. unsupervised ML models
  • Feature extraction from IoT sensor data
  • Hands-on: Building an anomaly detection model for industrial IoT

• Deep Learning for IoT Data

  • Using LSTMs, CNNs, and Transformers for IoT analytics
  • Training ML models with imbalanced IoT datasets
  • Hands-on: Applying deep learning for predictive maintenance

Afternoon Session

• Reinforcement Learning for IoT Automation

  • AI-driven control systems for smart grids & industrial automation
  • Reinforcement learning with IoT sensors
  • Hands-on: Optimizing energy consumption using reinforcement learning

• Hands-on Exercise

  • Applying machine learning to optimize IoT-driven supply chains

Day 4: Cloud Platforms, Security, & AI for IoT

Morning Session

• Cloud-Based IoT Analytics

  • IoT services on AWS, Azure, and Google Cloud
  • Integrating IoT data with BigQuery, AWS IoT Analytics, and Azure IoT Hub
  • Hands-on: Deploying an IoT analytics pipeline in the cloud

• Security, Privacy, and Governance for IoT Data

  • IoT cybersecurity risks and solutions
  • Implementing secure IoT data pipelines
  • Hands-on: Securing IoT data with encryption and access control

Afternoon Session

• AI-Driven Decision-Making for IoT

  • AI-powered IoT automation for smart cities, healthcare, and logistics
  • Federated learning and privacy-preserving AI for IoT
  • Hands-on: Building an AI-powered IoT dashboard

• Hands-on Exercise

  • Deploying an AI-driven smart city traffic management system

Day 5: End-to-End IoT Analytics Project & Deployment

Morning Session

• IoT Analytics in Action: Industry Case Studies

  • Smart Cities: Traffic optimization & pollution monitoring
  • Healthcare: Remote patient monitoring & AI-assisted diagnostics
  • Energy: Smart grid optimization & demand forecasting
  • Manufacturing: Industrial IoT & predictive maintenance

• AutoML for IoT Data Science

  • Google AutoML, AWS SageMaker, and Azure AI for IoT
  • Hands-on: Automating model selection and tuning for IoT analytics

Afternoon Session

• Capstone Project & Final Presentations

  • Choose from:
    1. Anomaly Detection for Smart Manufacturing IoT Devices
    2. AI-Based Traffic Management for Smart Cities
    3. Real-Time IoT Analytics for Energy Grid Optimization
  • Participants present their models & receive expert feedback

• Certification & Networking Session

Post-Course Benefits

• Hands-on experience with IoT data science tools & platforms
• Expertise in real-time analytics, edge computing, and AI for IoT
• Practical knowledge of cloud-based IoT solutions
• Portfolio-ready projects to showcase skills
• Access to exclusive datasets, Jupyter notebooks, and cheat sheets